A neurofeedback-assisted physical rehabilitation system and method

Abstract

The present invention relates to a neurofeedback-assisted physical rehabilitation system and method. The system comprises EEG headband (1), pneumatic glove (2), and central processing unit (3) with communication interface (4) to connect through user's mobile phone (5). This is a single-channel / multi-channel EEG headband and a pneumatic glove to facilitate neurofeedback-driven physical rehabilitation for patients with paralysis or muscular control issues. The system is provided with thought-controlled functionality enabling patients to regain motor control through neuroplasticity. Thus EEG-driven neurofeedback is combined with pneumatic actuators for real-time, thought-controlled physical rehabilitation. This offers a cost effective, non-invasive solution to regain muscular control through mental training providing, neurofeedback-based recovery

Description

A neurofeedback-assisted physical rehabilitation system and method

[0001] A neurofeedback-assisted physical rehabilitation system and method

[0002] The present invention relates to the field of physical rehabilitation system. The present invention in particular relates to a neurofeedback-assisted physical rehabilitation system and method for physical rehabilitation and mental wellness.

[0003] Neurofeedback is a method of informing a user of their brain activity in real time via a sensory feedback. To achieve the neurofeedback, an electroencephalogram (EEG) of the brain is captured in desired frequency ranges from appropriate brain locations and processed to generate a feedback signal. The generated feedback signal can be at least one of: an auditory, a visual or a haptic, and the like in nature. For example, an appearance of a visual cue in a neurofeedback session can be used to indicate rise in an attention level. Thus, the user is able to decipher information that the feedback signal carries. The neurofeedback works similar to operant conditioning, wherein behaviours that are enhanced over multiple training sessions.

[0004] Various features of brain function and activity can be trained using the neurofeedback. Some examples are attention, relaxation, resilience to stress and mood. The EEG of the brain is constituted by electromagnetic waves across various frequency ranges. Out of the electromagnetic waves, only those that are relevant to a feature of interest are selected as a biomarker. Further, the biomarker is averaged over a short initial time duration to derive average power of a waveform. During this initial time duration, there is no feedback provided. The average power is used to compute a threshold such that the user is having high activity rate whenever the threshold is crossed. The threshold is further altered to increase or decrease a difficulty level during the neurofeedback training session. The threshold could be regulated manually or using automation. Adjustment of the threshold becomes pivotal to make the session engaging and effective, if too hard, then the neurofeedback training session can discourage the user and if too easy, then the neurofeedback training session might lead to boredom.

[0005] The manual thresholding is performed by a neurofeedback specialist who monitors the biomarker levels and changes the threshold whenever the user crosses the threshold by a certain difference for a certain time duration. The manual thresholding can be inconsistent if not done under the same neurofeedback specialist and is further limited by the lack of trained personnel.

[0006] The automated thresholding is applied by averaging the biomarker over an overlapping or nonoverlapping time-window. The threshold is computed for each window and is converted into an activity rate administered at a certain rate (typically set at 70-80%). This is the biggest drawback of conventional methods, i.e., the feedback is based on a rate instead of the actual values of a digital biomarker. Usually, the real-time value of the digital biomarkers is in the range of normative distribution and a professional practitioner would not change the threshold letting the user work for the feedback. Further, fixing the activity rate does not allow conventional methods to take signal quality into account making those susceptible to noise artefacts. These caveats lead to the following issues: some of the users are not able to successfully change or train their brain activity, and the change in the brain activity happening in a direction opposite to an intended one, thus defeating the purpose of the neurofeedback.

[0007] Reference may be made to the following:

[0008] Publication No. US2024350071 relates to a method for generating a neurofeedback signal for neurofeedback session on low powered devices. The method includes capturing one or more bio-signals from one or more electronic devices, comprising one or more digital biomarkers; measuring a baseline activity associated with the captured one or more digital biomarkers during a resting state of the brain; measuring an activity rate based on the one or more digital biomarkers; computing a threshold value at a plurality of time series in the neurofeedback session based on at least one of: the measured activity rate and the one or more digital biomarkers captured during the baseline activity of the brain using an activity model; and (e) outputting of the neurofeedback signal corresponding to the neurofeedback session to the user based on the computed threshold value.

[0009] Publication No. TW202343186 relates to a smart headband for hand-swinging exercise comprises a wearable ring body and a circuit module. The wearable ring body has a display module, and the circuit module is provided inside the wearable ring body and is connected with the display module. The circuit module includes a brainwave sensing unit, a central processing unit, a storage unit and a power supply unit.

[0010] Publication No. KR20220168143 relates to a massage device including a device for traction of a neck portion. The massage device comprises: a main body in contact with a user's neck; an air cell installed in the main body and pressurizing the user's neck portion while expanding by air injected from the outside; a headband fixing the user's head portion not to move, and including a first band fixed to one side of the main body and a second band fixed to the other side of the main body; an elastic module installed at a lower part of the air cell to elastically support the expanded air cell when the air cell expands to increase force to pressurize a back portion of the user's neck upward and force to pull and stretch the user's neck portion, compared to a case in which there is no member supporting the expanded air cell when the air cell expands; and an air injection unit including an air compressor for pumping air and a hose for connecting the lower part of the air cell.

[0011] Publication No. US2021085234 relates to a brainwave headband structure, which includes a body and a brainwave detection module, wherein the body with an inner surface and an outer surface. The brainwave detection module is arranged on the outer surface of the body and is electrically connected with a first electrode, a second electrode and a third electrode.

[0012] Publication No. CN110090018 relates to a concentration degree analysis system based on an EEG headband, and is used for solving the problems of how to analyze and calculate the concentration degree through an EEG headband and display and how to store the concentration degree record reasonably.

[0013] Publication No. CN107028608 relates to a brainwave signal acquisition device and a preprocessing method thereof. The acquisition device comprises an elastic arc-shaped headband and a brainwave acquisition sensor arm connected to the elastic arc-shaped headband at both ends, wherein one end of the brainwave acquisition sensor arm is connected to a wireless transceiver module, and two ends of the elastic arc-shaped headband are connected to a jaw-fixing elastic soft belt used for making the elastic arc-shaped headband cling to the brain surface tightly; The brainwave acquisition sensor arm is electrically connected to at least three brainwave acquisition electrodes..

[0014] Publication No. KR20130087653 relates to a wireless headband module for measuring a brainwave signal in a scalp to implement accurate measurement by using a sensor coated by carbon nanotube materials. A carbon nanotube is coated on an electrode unit. The electrode unit measures a brainwave coming from a scalp.

[0015] Publication No. AU2018250529 relates to an apparatus for determining and monitoring neurological or muscular disorder, comprising of: sensors for acquiring electro-myographic (EMG) signals of a subject; wherein the EMG signals are used to determine REM stages of sleep; and wherein the EMG signals are used to determine the presence of atonia; and the EMG signals are used to determine the presence of REM sleep behavior disorder (such as Parkinson's, restless leg, leg movement syndrome, dystonia, Wilson's disease, Huntington, Alzheimer or the like) and the sensors are for determining the electroencephalographic (EEG) signals of subject, or they further comprising determining elecro-oculogram (EOG) signals or motion signals. The apparatus is also used in monitoring muscular disorder that is a Parkinson's disease's disorders.

[0016] Publication No. US2019183401 relates to a hemoencepholagraphy neurofeedback device includes a headband assembly, and a circuit board coupled to said headband strap. The circuit board includes at least a pair of light emitting diodes, a receiver and a microprocessor. The pair of diodes includes a red light and an infrared light. When the pair of light emitting diodes are emitted onto a user's forehead, the receiver measures the amount of light returned from the forehead, and a display device or software application displays the microprocessor's calculation of the amount of blood flow in the area of the user's forehead.

[0017] Publication No. US2005197556 relates to an neurofeedback device for comfortably holding at least one electrode onto a patient's head is described. The device includes an electrode positioning device, such as a headband and one or more straps each connected at spaced points of the headband and extending over at least a portion of the patient's head. At least one electrode holder is carried by the electrode positioning device for holding an electrode in electrical contact with the patient's head.

[0018] Patent No. US6454681 relates to an exercise and therapy hand device consists of a glove for receiving a hand. The glove includes a palm portion, one or more finger portions for receiving a finger, a back portion, and a compressible substance that is coupled to the palm portion. Also included in the glove are one or more elastic members that span from the compressible substance to respective ones of the one or more finger portions. The compressible substance exercises the muscles of the hand that control the closing of the hand, while the one or more elastic members exercise the muscles of the hand that control the opening of the hand.

[0019] Publication No. EP2884885 relates to a neurofeedback system that comprises an electrode (2, 2', 2'') for contacting skin of a user for measuring a biofeedback signal of the user a first signal processing unit for determining a signal characteristic of the measured biofeedback signal wherein the signal characteristic represents a neurofeedback, a second signal processing unit for determining a biofeedback signal quality of the measured biofeedback signal by extracting a signal feature of the measured biofeedback signal and calculating a probability of a measurement error for said signal feature, which probability represents the biofeedback signal quality and a feedback unit for providing feedback to the user, wherein the feedback comprises the neurofeedback and a feedback about the biofeedback signal quality.

[0020] Publication No. CN220424166 relates to a control device for brain-controlled hand function rehabilitation training. The control device comprises a pneumatic control box, the pneumatic control box comprises a shell, a control circuit board, a relay, an air pump and an electromagnetic valve, and a cavity is formed in the shell; the control circuit board is arranged in the cavity; the relay and the control circuit board are arranged in the cavity at an interval, and the relay is electrically connected to the control circuit board; the air pump is arranged in the cavity and is electrically connected to the relay; the electromagnetic valve and the air pump are arranged in the cavity in a spaced mode, and the electromagnetic valve communicates with the air pump through an air pipe.

[0021] Publication No. CN220360529 relates to an active rehabilitation system based on a brain-computer interface. The active rehabilitation system comprises a brain cap for a patient to wear and pneumatic gloves, the brain cap comprises an electroencephalogram signal acquisition chip, an analog-to-digital converter and a signal identification chip which are electrically connected in sequence, and is used for acquiring an electroencephalogram signal of a patient and identifying the electroencephalogram signal into a motion signal after analog-to-digital conversion; the pneumatic glove comprises a driving device and an air pressure control device, and the driving device is in communication connection with the signal identification chip and is used for driving the pneumatic glove to perform corresponding actions according to the identified motion signal; and the air pressure control device is used for regulating and controlling the air pressure of the pneumatic glove.

[0022] Patent No. US4072145 relates to a headband assembly for sensing signals of brain wave frequencies of human subjects that includes an adjustable headband adapted to fit various sized heads, constructed to be quickly attached and detached by the wearer, and including electrodes that are shiftable along the headband and of a construction to penetrate the hair of the subject in making skin contact.

[0023] Patent No. US7698909 relates to a headband having a low stretch segment sized to fit around a wearer's head, and an elastic segment being smaller than the low stretch segment, and having a free end and an attached end, where the elastic segment is attached at its attached end with the low stretch segment. The headband also includes a tab portion having a first end and a second end, the first end of the tab portion being connected with the free end of the elastic portion, the second end of the tab portion configured to form a closed loop with the low stretch segment, around the wearer's head.

[0024] Publication No. US2021361222 relates to neurofeedback devices, systems, and methods for pain management. In some embodiments, the system includes one or more headset configured for detecting electrical activity of a brain of the subject; one or more electronic device in electronic communication with the one or more headset, the one or more electronic device comprising one or more processors configured to operate one or more software applications, which, when operated by the one or more processors, configure the one or more electronic device to perform the following functions: receive and analyze electrical activity data of the subject's brain from the one or more headset; and transmit an output to the subject, the output having a type selected by the subject, via a sound or visual output device based on an analysis of the received electrical activity data of the subject's brain; wherein the output is configured to reduce or relieve pain in the subject.

[0025] Publication No. IN202411058220 relates to a neuroresonance relaxation system which promotes mental wellness by harnessing advanced neurotechnology to induce relaxation and reduce stress. Comprising electroencephalogram (EEG) sensors, signal processing software, neurofeedback equipment, binaural beats or isochronic tones generators, light and visual stimulation devices, biofeedback devices, headband, power source and user interface, this device offers a comprehensive system for promoting mental well-being. EEG Sensors detect brainwave activity, while signal processing software analyzes and interprets data in real-time.

[0026] Publication No. IN4408 / CHENP / 2009 relates to an apparatus for determining the mental state of a user, the apparatus comprising: a frame; one or more dry-active sensors located on the frame that are capable of detecting the brain waves of a user when the sensors touch a skin portion of a user and of generating brain wave signals; and a processing unit configured to: receive the brain wave signals; process the brain wave signals to generate a level of a mental state, comprising to: convert the brain wave signals into a set of digital brain wave signals; process the digital brain wave signals to determine a level of a mental state of the user, comprising to: analyze the digital brain wave signals to extract delta, theta, alpha and beta waves; extract power spectrum data for the delta, theta, alpha and beta waves; and determine the level of the mental state based on the extracted power spectrum data of the delta, theta, alpha and beta waves; and generate a signal corresponding to the level of the mental state of the user, wherein the mental state includes at least one of attention, relaxation, meditation, anxiety, and drowsiness.

[0027] Publication No. IN202411023409 relates to a neurofeedback training system for treating attention deficit hyperactivity disorder (ADHD) in a subject. The system comprises a brain-computer interface (BCI) that acquires real-time electroencephalographic (EEG) data from the subject, a processing unit that extracts relevant EEG features indicative of ADHD symptoms, a feedback module that generates neurofeedback stimuli based on the extracted EEG features, and a display unit that presents the neurofeedback stimuli to the subject. The system trains the subject to modulate their brain activity in response to the stimuli, reducing ADHD symptoms.

[0028] Patent No. US4928704 relates to a biofeedback method and system for use to train a person to develop useful degrees of voluntary control of personal electroencephalogram (EEG) activity. A plurality of EEG potentials from a plurality of locations on the head are individually amplified and filtered in accordance with strict criteria intended for processing in accordance within time constraints limited by natural neurological reactivity. Each resultant signal is processed to provide objective data on brain energies as a function of frequency. Data are presented in real time to the trainee in the form of preselected auditory tones and / or vibro-tactile stimuli indicating with high fidelity the details of EEG activity at a multiplicity of cortical sites.

[0029] Patent No. US6097981 relates to an apparatus and method with an electroencephalograph (EEG) based biofeedback system wherein a smooth, high quality computer animation is maintained while EEG responses are simultaneously being analyzed whereby the results of the analysis are then used to control the animation. EEG signals alone may be used to control computer animation. EEG signals may be sent from the head of the user to a remote receiver by infrared wireless transmission.

[0030] Publication No. CN102579228 relates to a pair of pneumatic hand function rehabilitation gloves. Each pneumatic hand function rehabilitation glove comprises a glove body, an electric inflating and deflating device and a pneumatic muscle, wherein the tail end of the pneumatic muscle is connected with the electric inflating and deflating device. The pneumatic muscle is arranged at the back side of the finger part of the glove body, and the ventral side of the pneumatic muscle is provided with an elastic steel disc. The back side of the pneumatic muscle is provided with an arc-shaped hose, and the ventral side of the arc-shaped hose is stuck with the elastic steel disc.

[0031] Publication No. CN111789745 relates to a pneumatic rehabilitation glove, and the technical field of rehabilitation auxiliary medical instruments. A pneumatic artificial muscle is arranged on a flexible palm rest to form a hand-shaped structure, early extension movement and thumb abduction movement of a stroke patient can be assisted, the pneumatic artificial muscle is of an arc-shaped structure, can be well attached to the finger, in the contracture state, of a stroke patient, and can be effectively replace manual operation training. Time and labor are saved, over-stretching strength can be adjusted for patients of different degrees, and a good exercising effect can be achieved. The device has the advantages of being reasonable in structural layout, good in using effect and the like and is suitable for large-scale application and popularization.

[0032] The article entitled “Neuphony headband” by Pankhtech India Private Limited.; neuphony; 2024 talks about the neuphony EEG headband is a neurofeedback-based wearable brain device that captures key mind parameters through conductive polymer sensors. Track your cognitive insights, experience neurofeedback at home, and improve your brain health. Pair your headband with the mobile app for performance scores or the desktop app for detailed reports, including EEG data and cognitive insights. Do a Pre-Yoga and Post-Yoga analysis or track your real-time brain data while you perform different asanas.

[0033] The article entitled “A wireless EEG system for neurofeedback training” by talks about the mobile, easy-to-maintain wireless electroencephalograph (EEG) system designed for work with children in a school environment. This EEG data acquisition platform is a small-sized, battery-powered system with a high sampling rate that is scalable to different channel numbers. The system was validated in a study of live z-score neurofeedback training for quantitative EEG (zNF-qEEG) for typical-reading children and those with developmental dyslexia (DD). This system reads and controls real-time neurofeedback (zNF) signals, synchronizing visual stimuli (low spatial frequency (LSF) illusions) with the alpha / theta (z-α / θ) score neural oscillations. The NF sessions were applied during discrimination of LSF illusions with different contrasts. Visual feedback was provided with color cues to remodulate neural activity in children with DD and their cognitive abilities. The combined zNF-qEEG and training with different visual magnocellular and parvocellular tasks (VTs) compensated for the deficits in the temporal areas affecting the occipitotemporal pathway more in the left-hemispheric ventral brain areas of the post-training children with dyslexia in the low-contrast LSF illusion and dorsal dysfunction in the high-contrast LSF illusion. The better α / θ scores for postD in the temporoparietal and middle occipital regions can be associated with an improvement in special frequency processing, while the better scores in the precentral and parietal cortices were due to an advancement in the temporal processing of the illusion. The improvements in the reading speeds were twice as high after 4 months of qEEG z-NF-VT training, with three times fewer omitted words and errors.

[0034] The article entitled “Towards a soft pneumatic glove for hand rehabilitation” by Tsvetalin Totev, Tihomir Taskov and Juliana Dushanova ; Appl. Sci. 2023, 13(1), 96; 23 September 2022 talks about the development and evaluation of a hand rehabilitation glove fabricated using soft robotic technology. Soft actuators comprised of elastomeric materials with integrated channels that function as pneumatic networks (PneuNets), are designed and geometrically analyzed to produce bending motions that can safely conform with the human finger motion. Bending curvature and force response of these actuators are investigated using geometrical analysis and a finite element model (FEM) prior to fabrication. The fabrication procedure of the chosen actuator is described followed by a series of experiments that mechanically characterize the actuators. The experimental data is compared to results obtained from FEM simulations showing good agreement. Finally, an open-palm glove design and the integration of the actuators to it are described, followed by a qualitative evaluation study.

[0035] The article entitled “A game-based neurofeedback training system to enhance cognitive performance in healthy elderly subjects and in patients with amnestic mild cognitive impairment” by Suwicha Jirayucharoensak, Pasin Israsena, Setha Pan-ngum, Solaphat Hemrungrojn, Michael Maes; Clin Interv Aging.;14:347–360; 2019 Feb 19 talks about the clinical efficacy of a game-based neurofeedback training (NFT) system to enhance cognitive performance in patients with amnestic mild cognitive impairment (aMCI) and healthy elderly subjects. The NFT system includes five games designed to improve attention span and cognitive performance. The system estimates attention levels by investigating the power spectrum of Beta and Alpha bands. NFT significantly improved rapid visual processing and spatial working memory (SWM), including strategy, when compared with exergame training and no active treatment. aMCI was characterized by impairments in SWM (including strategy), pattern recognition memory, and delayed matching to samples.

[0036] The article entitled “Designing mobile EEG neurofeedback games for children with autism: implications from industry practice” by Zhaoyi Yang, Pengcheng An, Jinchen Yang, Samuel Strojny, Zihui Zhang, Dongsheng Sun, Jian Zhao; ACM International Conference on Mobile Human-Computer Interaction (Industry Perspectives), pp. 23:1; 27 September 2021 talks about the neurofeedback games are an effective and playful approach to enhance certain social and attentional capabilities in children with autism, which becomes increasingly accessible with commercialized mobile EEG modules. However, little industry-based experiences are shared, regarding how to better design neurofeedback games to fine-tune their playability and user experiences for autistic children. In this paper, we review the experiences we gained from industry practice, in which a series of mobile EEG neurofeedback games have been developed for preschool autistic children. We briefly describe our design and development in a one-year collaboration with a special education center involving a group of stakeholders: children with autism and their caregivers and parents. We then summarize four concrete implications we learnt concerning the design of game characters, game narratives, as well as gameplay elements, which aim to support future work in creating better neurofeedback games for preschool children with autism.

[0037] The article entitled “Neuphony” by indiascienceandtechnology; 2024 talks about the neuphony works on the unlimited potential of the human mind and aims to unlock it. Neurofeedback has been scientifically proven to be a powerful tool to improve the brain’s overall health, including improving memory, sleep quality, and reducing stress and anxiety. Neuphony brings the benefits of neurofeedback to the comfort of people’s homes with the help of an Electroencephalogram (EEG) headband and a mobile application. The headband measures the brain’s electrical activity, and based on the sensor data, the mobile app recommends meditation techniques to achieve the desired state. Over multiple sessions, it trains the brain on how to achieve the desired state itself.

[0038] The article entitled “Design of wearable hand rehabilitation glove with soft hoop-reinforced pneumatic actuator” by Zhongsheng Sun, Zhong-hua Guo, Wei Tang; Journal of Central South University 26(1):106-119; January 2019 talks about the traditional hand rehabilitation gloves usually use electrical motor as actuator with disadvantages of heaviness, bulkiness and less compliance. Recently, the soft pneumatic actuator is demonstrated to be more suitable for hand rehabilitation compared to motor because of its inherent compliance, flexibility and safety. In order to design a wearable glove in request of hand rehabilitation, a soft hoop-reinforced pneumatic actuator is presented. By analyzing the influence of its section shape and geometrical parameters on bending performance, the preferred structure of actuator is achieved based on finite element method. An improved hoop-reinforced actuator is designed after the fabrication and initial measurement, and its mathematical model is built in order to quickly obtain the bending angle response when pressurized. A series of experiment about bending performance are implemented to validate the agreement between the finite element, mathematical and experimental results, and the performance improvement of hoop-reinforced actuator. In addition, the designed hand rehabilitation glove is tested by measuring its output force and actual wearing experience. The output force can reach 2.5 to 3 N when the pressure is 200 kPa. The research results indicate that the designed glove with hoop-reinforced actuator can meet the requirements of hand rehabilitation and has prospective application in hand rehabilitation.

[0039] The article entitled “Active triggering control of pneumatic rehabilitation gloves based on surface electromyography sensors” by Yongfei Feng, Mingwei Zhong, Xusheng Wang, Hao Lu, Hongbo Wang, Pengcheng Liu, Luige Vladareanu; PeerJ Comput Sci.:7:e448 ; 2021 Apr 19 talks about the portable and inexpensive hand rehabilitation robot has become a practical rehabilitation device for patients with hand dysfunction. A pneumatic rehabilitation glove with an active trigger control system is proposed, which is based on surface electromyography (sEMG) signals. It can trigger the hand movement based on the patient’s hand movement trend, which may improve the enthusiasm and efficiency of patient training. Firstly, analysis of sEMG sensor installation position on human’s arm and signal acquisition process were carried out. Then, according to the statistical law, three optimal eigenvalues of sEMG signals were selected as the follow-up neural network classification input. Using the back propagation (BP) neural network, the classifier of hand movement is established. Moreover, the mapping relationship between hand sEMG signals and hand actions is built by training and testing. Different patients choose the same optimal eigenvalues, and the calculation formula of eigenvalues’ amplitude is unique. Due to the differences among individuals, the weights and thresholds of each node in the BP neural network model corresponding to different patients are not the same. Therefore, the BP neural network model library is established, and the corresponding network is called for operation when different patients are trained. Finally, based on sEMG signal trigger, the pneumatic glove training control algorithm was proposed. The combination of the trigger signal waveform and the motion signal waveform indicates that the pneumatic rehabilitation glove is triggered to drive the patient’s hand movement. Preliminary tests have confirmed that the accuracy rate of trend recognition for hand movement is about 90%. In the future, clinical trials of patients will be conducted to prove the effectiveness of this system.

[0040] The present invention relates to a neurofeedback-assisted physical rehabilitation system and method. This is a single-channel / multi-channel EEG headband and a pneumatic glove to facilitate neurofeedback-driven physical rehabilitation for patients with paralysis or muscular control issues. The invention incorporates neuroplasticity principles to retrain the brain for muscular control.

[0041] Thus existing technologies either focus on physical rehabilitation or mental wellness independently.

[0042] Hence there needed a system and method which can focus on physical rehabilitation and mental wellness both.

[0043] In order to overcome limitations found in the above listed prior art, the present invention aims to provide a neurofeedback-assisted physical rehabilitation system and method

[0044] The principal object of the present invention is to provide a single-channel / multi-channel EEG headband and a pneumatic glove to facilitate neurofeedback-driven physical rehabilitation for patients with paralysis or muscular control issues

[0045] Still another object of the present invention is to provide EEG-based neurofeedback with a pneumatic glove for physical rehabilitation.

[0046] Yet another object of the present invention is to provide neurofeedback-assisted physical rehabilitation system with thought-controlled functionality enabling patients to regain motor control through neuroplasticity.

[0047] Still another object of the present invention is to provide neurofeedback-assisted physical rehabilitation system which employs AI-based real-time adaptive thresholding, which continuously adjusts based on EEG patterns and engagement levels to ensure optimal difficulty for the user.

[0048] In an advantageous embodiment, the neurofeedback-assisted physical rehabilitation system and method offers a cost effective, non-invasive solution to regain muscular control through mental training providing, neurofeedback-based recovery.

[0049] It is to be noted, however, that the appended drawings illustrate only typical embodiments of this invention and are therefore not to be considered for limiting of its scope, for the invention may admit to other equally effective embodiments.

[0050]

[0051] reveals block diagram according to the present invention

[0052]

[0053] reveals flowchart according to the present invention

[0054] The present invention provides a neurofeedback-assisted physical rehabilitation system and method. This is a single-channel / multi-channel EEG headband and a pneumatic glove to facilitate neurofeedback-driven physical rehabilitation for patients with paralysis or muscular control issues. The invention incorporates neuroplasticity principles to retrain the brain for muscular control.

[0055] The invention combines a lightweight EEG headband and a pneumatic glove to create a neurofeedback-assisted rehabilitation system. Patients wear the EEG headband, which detects brainwave activity, and use the pneumatic glove to regain muscular control through guided thought processes. The device leverages neuroplasticity and real-time neurofeedback to stimulate motor recovery.

[0056] A mobile app integrates with the system for cognitive training and mental wellness, offering neurofeedback games, meditation exercises, and progress tracking to complement physical rehabilitation

[0057] Referring to, the system comprises EEG headband (1), pneumatic glove (2), and central processing unit (3) with communication interface (4) to connect through user’s mobile phone (5).

[0058] EEG Headband (Single-Channel / Multi-Channel) (1) placed FP1 at position on the forehead captures brainwave signals from the FP1 position on the forehead. It uses ground electrodes on earlobes and positive / reference electrodes on the forehead and Computes cognitive metrics like attention, stress, and meditation. Ground electrodes on the earlobes, positive and reference electrodes on the forehead. It captures brainwave signals in key frequency bands (Alpha, Beta, Theta, Delta, Gamma), computes cognitive metrics like attention, stress, and meditation and sends processed EEG signals to the CPU for real-time processing.

[0059] Central Processing Unit (CPU) (3) processes EEG signals from the headband via bluetooth(BLE) in real-time, detects motor intent based on specific neural activity patterns. If motor intent is detected, a signal is sent to the pneumatic glove and if no intent is detected, the system continues monitoring.

[0060] Pneumatic Glove (2) are fitted with soft actuators that assist in controlled hand movements, responds to EEG-based neural activity. It inflates / deflates in response to brain signals, mimicking hand movements and provides haptic feedback to reinforce motor pathways and enhance neuroplasticity.

[0061] Communication Interface (4) connects the CPU to the pneumatic glove and ensures real-time transmission of motor intent signals to control the glove’s movements.

[0062] Mobile App (5) provides mindfulness sessions & gamified neurofeedback: and provides supplementary exercises such as guided meditation and focus-enhancing neurofeedback games. It enhances cognitive reinforcement to aid in motor recovery. It is not connected to the CPU or glove system.

[0063] Haptic feedback loop (6) provides sensory reinforcement. Glove movement provides haptic feedback to the brain, strengthens neural pathways through repetitive intent-action-feedback cycles and reinforces cognitive-motor connections, accelerating neuroplasticity.

[0064] Closed-Loop System (7) sends EEG signals to CPU via Bluetooth (BLE), CPU detects motor intent , Pneumatic glove moves provides Haptic feedback and reinforces neural pathways.

[0065] Pneumatic rehabilitation gloves are fitted with soft actuators that assist in controlled hand movements, responds to EEG-based neural activity to stimulate movement through neuroplasticity and provides haptic feedback, reinforcing cognitive-motor pathways.

[0066] Central processing unit (CPU) processes EEG data in real-time. It applies adaptive AI-driven thresholding for neurofeedback, translates brain activity into glove movement signals.

[0067] Mobile Application (Optional but Enhances Functionality) offers neurofeedback games, meditation exercises, and cognitive training, tracking patient progress and provides visual and auditory neurofeedback for engagement.

[0068] A single-channel EEG device placed at the FP1 position to record brainwave activity. It includes ground electrodes on the earlobes and positive / reference electrodes on the forehead. The headband computes attention, stress, meditation, and other cognitive metrics. Multi-channel EEG devices can also be used. This is part of the 10-20 electrode placement system used in EEG. FP1 is located on the left side of the forehead, near the prefrontal cortex, which is associated with higher cognitive functions such as focus, decision-making, and emotional regulation.

[0069] Ground Electrodes clipped to earlobe to provide a reference point and reduce noise. Positive / Reference Electrodes are placed on the forehead to measure voltage changes caused by neural activity. It computes metrics, measures how engaged and focused the user is, analyzes brainwave patterns to detect stress levels, identifies calm and relaxed states. Device can also calculate blink rate, fatigue, and cognitive load. The device detects electrical activity generated by neurons in the brain. It converts analog signals into digital data and processes it to compute cognitive metrics. The data is transmitted wirelessly via Bluetooth to a mobile or desktop application for visualization and analysis.

[0070] The glove fitted with actuators to simulate controlled movements of paralyzed limbs. It responds to signals from the EEG headband, training the brain to reestablish motor pathways.

[0071] The headband communicates via Bluetooth to a central processing unit, which translates thought patterns into pneumatic glove movements.

[0072] A mobile application supports cognitive training through neurofeedback games and tracks patient progress. The app is not essential for physical rehabilitation but enhances the system’s overall utility.

[0073] The system is provided with thought-controlled functionality enabling patients to regain motor control through neuroplasticity. The system actively modifies glove movements based on EEG signals rather than just pre-programmed responses which ensures that rehabilitation is not just mechanical but also neurological, reinforcing motor-intent-based rehabilitation through neurofeedback

[0074] The system integrates EEG-based neurofeedback with a pneumatic glove to help patients, especially stroke or paralysis patients, regain motor control by enhancing neural plasticity. When a person imagines or attempts a movement, even if the movement cannot be physically executed, neural pathways associated with that movement are activated. Instead of passively exercising the muscles through pre-programmed movements, the system reinforces motor control by aligning glove movement with the patient’s brain activity.

[0075] The EEG headband, placed at the FP1 position, detects brain activity related to motor intention (the thought or effort to move a limb).Signals related to motor imagery or intention are captured and processed.The system processes EEG signals using a machine learning model to identify motor intent patterns.

[0076] It distinguishes between different types of intent, such as opening or closing the hand. Upon detecting the motor intent, the pneumatic glove inflates or deflates accordingly to assist with hand movements. The glove doesn’t just follow a pre-programmed sequence but dynamically adjusts its movements based on real-time EEG signals, creating a feedback loop.

[0077] When the glove moves in sync with the patient’s thoughts, visual, tactile, and proprioceptive feedback is provided. This feedback strengthens the brain’s connection to the affected limb by reinforcing the intended movement through neuroplasticity. The device moves passively, the system encourages the brain to stay actively involved in the movement. Consistent thought-based activation of the motor cortex builds stronger neural pathways, improving voluntary control over time. As the glove adapts to the user's brain signals, it tailors the rehabilitation process, making it more effective for individual patients. Neurofeedback ensures that the brain relearns motor control faster, leading to long-term improvements even after therapy.

[0078] Error Correction: When the movement deviates from the intended motion, the system provides corrective feedback to fine-tune the brain's control over the limb. Positive Reinforcement: When the correct motion is achieved, the brain receives a reward (visual or haptic feedback), strengthening the neural pathway.

[0079] A closed-loop approach is provided, where brain signals influence glove movement, and glove movement provides haptic feedback to reinforce motor pathways. This real-time loop significantly improves brain-muscle coordination and accelerates rehabilitation compared to traditional neurofeedback-only or robotic-rehabilitation-only systems. The EEG headband placed at the FP1 position detects motor intention signals from the brain.

[0080] These signals are processed to identify whether the patient intends to open, close, or move the hand. Upon detecting the motor intention, the pneumatic glove inflates or deflates to assist in performing the intended movement. The glove doesn’t just follow a predefined routine — it reacts in real-time based on the brain’s command, ensuring personalized and dynamic responses.

[0081] As the glove moves, it provides haptic (tactile) feedback to the patient’s hand, signaling the brain that the intended movement has occurred. This tactile sensation stimulates sensory receptors, creating a feedback loop that reinforces the brain’s motor pathways.

[0082] If the glove’s movement deviates from the intended action, the system adjusts in real time, providing corrective feedback. This ensures that the brain refines its motor control with each iteration.

[0083] shows the flow chart. The headset listens the brains signal, AI analyzes the patterns and signal and translates it to movement command. The thoughts triggers the precise hand movements through smart responsive glove.

[0084] The loop ensures real-time synchronization between the brain and the glove. As the brain sends signals to move, the glove moves, and the brain immediately perceives the movement through sensory feedback, reinforcing the neural pathways involved.

[0085] The combination of neurofeedback (brain signal analysis) and haptic feedback (touch sensations) enhances neuroplasticity by engaging both motor and sensory pathways. This dual feedback strengthens motor-intent learning by closing the sensory-motor loop.Repeated cycles of motor intent, movement, and feedback create stronger neural connections between the brain and muscles, accelerating recovery. Since the brain perceives its control over the movement, it rewires faster compared to passive robotic rehabilitation. The EEG headband detects brain activity in key frequency ranges (Alpha, Beta, Theta, Delta, Gamma). The user is guided to focus on hand movement while wearing the EEG device

[0086] The EEG headband, placed at the FP1 position (forehead), detects electrical activity generated by neurons in the brain. It analyzes this activity across five key frequency ranges associated with different mental states and cognitive processes. A moderate level of alpha waves suggests a calm but alert state, ideal for initiating motor imagery and maintaining focus during rehabilitation. Higher beta activity indicates concentration and cognitive processing, often associated with motor planning and execution.

[0087] Higher Beta = Stronger Motor Intention Signals.

[0088] Theta Waves (4–7 Hz) are associated with deep relaxation, mental imagery, and meditation. Theta waves often appear when the user is visualizing or imagining movement, contributing to motor imagery training.

[0089] Delta Waves (0.5–3 Hz) are primarily seen during deep sleep and unconscious states. Minimal relevance during active motor tasks, but background delta can indicate resting states.

[0090] Gamma Waves (30–100 Hz) are linked to high-level cognitive functions, sensory processing, and focused attention. Gamma activity increases when the brain processes motor imagery and prepares for voluntary movements.

[0091] The system analyzes brainwave activity related to motor intent. AI-driven adaptive thresholding adjusts difficulty to keep the user engaged. Real-time visual / auditory feedback is provided through the mobile app. This trains the brain to generate consistent motor intent signals through operant conditioning ().

[0092] When the system detects sufficient motor intent, it sends a Bluetooth signal to the pneumatic glove. The glove inflates / deflates in response to brain signals, mimicking hand movements.

[0093] The user gradually regains voluntary control as brain-muscle connections strengthen through neuroplasticity.

[0094] Over multiple sessions, the brain adapts to control movements via neural pathways. The EEG-driven feedback and pneumatic-assisted movements synchronize, accelerating motor recovery.

[0095] The user is instructed to imagine or attempt hand movements while wearing the EEG device. This triggers motor imagery, activating the motor cortex and generating distinct patterns of beta and gamma waves. The EEG device captures these brainwave patterns and identifies the user’s motor intent. Higher beta and gamma activity suggest that the / is focusing on a specific movement, such as opening or closing the hand. Upon detecting the motor intent, the pneumatic glove inflates or deflates to assist the user in performing the imagined or intended hand movement.

[0096] Stronger Motor Intention = Faster and More Accurate Glove Response.

[0097] As the glove responds, the system provides haptic feedback (tactile sensation) to the hand. This feedback reinforces the brain’s perception of successful movement, enhancing neuroplasticity and strengthening the connection between the brain and muscles. The method includes following steps:

[0098] The EEG headband placed at the FP1 position monitors brainwave activity. When the user focuses on moving their hand (either through actual movement or motor imagery), specific brainwave patterns and frequencies emerge. The system continuously analyzes these signals and determines whether they indicate a genuine intent to move the hand.

[0099] Once the system detects sufficient motor intent, it triggers a wireless command via Bluetooth. The signal contains instructions for the pneumatic glove to perform the intended movement. The Bluetooth module inside the glove’s control unit receives this signal and activates the corresponding function (inflation or deflation).

[0100] The pneumatic glove is designed to replicate natural hand movements by using air pressure-based actuation. If the brain signals indicate an open-hand intent, the system inflates specific air chambers inside the glove. This applies outward force, gently assisting the user’s fingers in extending.

[0101] If the system detects an intent to grasp or close the hand, the glove deflates, reducing pressure and allowing the fingers to flex inward. The glove’s movement is not just binary (on / off); instead, it can adjust inflation levels based on the strength of motor intent detected by the EEG system. Stronger brain signals, faster or firmer is the movement of the glove.

[0102] The mobile application provides scoring, challenges, and real-time progress tracking, keeping users engaged.

[0103] Thus EEG-driven neurofeedback is combined with pneumatic actuators for real-time, thought-controlled physical rehabilitation. This offers a cost effective, non-invasive solution to regain muscular control through mental training providing, neurofeedback-based recovery.

[0104] 1 – comprises EEG headband ( (3) with

[0105] 2 – pneumatic glove

[0106] 3 – central processing unit

[0107] 4 – communication interface to connect through user’s mobile phone (5).

[0108] 5– user’s mobile phone

Claims

A neurofeedback-assisted physical rehabilitation system comprisesEEG headband (1) to captures brainwave signals from the FP1 position on the forehead characterized in that ground electrodes on earlobes and positive / reference electrodes on the forehead and Computes cognitive metrics like attention, stress, and meditation wherein system translates EEG brain signals into real-time pneumatic glove movements, dynamically responding to the user’s motor intent.pneumatic glove (2), fitted with soft actuators that assist in controlled hand movements, responds to EEG-based neural activity to stimulate movement through neuroplasticity and provides haptic feedback, reinforcing cognitive-motor pathways.central processing unit (3) with communication interface (4) to connect through user’s mobile phone (5) processes EEG data in real-time wherein All EEG data processing and glove control are handled by an independent central processing unit (CPU) without requiring connection to a mobile device.The neurofeedback-assisted physical rehabilitation system, as claimed in claim 1, wherein the EEG Headband is single-channel or multi-channelThe neurofeedback-assisted physical rehabilitation system, as claimed in claim 1, wherein the Central processing unit (CPU) applies adaptive AI-driven thresholding for neurofeedback, translates brain activity into glove movement signalsThe neurofeedback-assisted physical rehabilitation system, as claimed in claim 1, wherein the closed-loop including brain signals influencing glove movement, which provides haptic feedback to reinforce motor pathways.The neurofeedback-assisted physical rehabilitation system, as claimed in claim 1, wherein the system analyzes brainwave activity related to motor intent. AI-driven adaptive thresholding adjusts difficulty to keep the user engaged, provides real-time visual / auditory feedback through the mobile application which trains the brain to generate consistent motor intent signals through operant conditioning.The neurofeedback-assisted physical rehabilitation system, as claimed in claim 1, wherein when the system detects sufficient motor intent, it sends a Bluetooth signal to the pneumatic glove which inflates / deflates in response to brain signals, mimicking hand movements.

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