11 results about "Steady state visually evoked potential" patented technology

This invention discloses a brain-controlled cotton candy machine control method based on attention level and SSVEP (Steady State Visual Evoked Potential), belonging to the field of brain-computer interface and food processing equipment integration technology. This invention employs a NeuroSci wireless EEG acquisition system, using SSVEP steady-state visual evoked potentials to achieve flavor selection (original, strawberry, pineapple), and utilizes frontal electrodes to collect EEG signals to calculate attention level. An STM32F407ZGT6 is used as the main controller to establish a precise mapping relationship between attention level grading and the cotton candy machine's motor speed. This is combined with servo motors for automatic quantitative feeding, infrared monitoring for candy card reversal and obstacle clearance, and touch display, voice commands, and multimodal audio-visual feedback. A state machine logic is used to complete the entire process of automatic control, from initialization and preheating to interaction, production, and completion. This invention achieves closed-loop control of "EEG acquisition—signal analysis—device execution," solving the problems of cumbersome operation, poor interactivity, and limited functionality in traditional cotton candy machines. It features intelligent operation, high stability, and high safety, and can be used in parent-child interaction, brain science popularization, and attention training scenarios, possessing high application and promotion value.

This invention relates to a brain-computer interface (BCI) control method and system for a rehabilitation robot, comprising the following steps: S1: Using a target image stimulation paradigm, visual evoked signals are induced in the operator, and the operator's electroencephalogram (EEG) signals are simultaneously acquired. The EEG signals include two types: a first type is steady-state visual evoked potential (VEP) EEG signals, and a second type is hand fine motor intention EEG signals; S2: The steady-state VEP EEG signals are decoded to identify the target object and its spatial position in the image being viewed by the operator, generating a target selection command. By employing the above technical solution, this invention simultaneously acquires steady-state VEP EEG signals and hand fine motor intention EEG signals, which are used for target position selection and robot hand movement type determination, respectively. This achieves decision fusion of dual-modal EEG signals, overcoming the problems of low dimensionality and poor flexibility in single-mode control.

According to various aspects, a new concept of Steady-State Visually Evoked Potential (SSVEP) based Brain-Computer Interface (BCI) is described where brain-computer communication occurs by capturing SSVEP induced by consciously imperceptible visual stimuli integrated into, for example, a virtual scene. These consciously imperceptible visual stimuli are able to convey subliminal information to a computer. In various embodiments, computer based operations can be mapped to visual elements with associated flickering stimuli, and induced SSVEP can be detected when the user focused upon them. In various embodiments, these visual elements can be introduced into existing display without any perceivable change to content being displayed.

This invention discloses a control system and method for Internet of Things (IoT) devices based on steady-state visual evoked potentials (SSVEP) and augmented reality (AR). The system is applied to wearable AR devices and includes an SSVEP signal acquisition module, an AR display and stimulus presentation module, a processing and recognition module, a wireless communication and control module, and an AR feedback presentation module. The AR module renders a virtual control object with a specific flickering frequency in the user's field of vision, while the acquisition module acquires EEG signals from the user's occipital cortex in real time. The processing and recognition module decodes the user's gaze intent and generates control commands using CCA or deep learning algorithms. Furthermore, the system integrates eye tracking for dual intent verification and utilizes a context-aware module to adaptively switch scene modes. This invention achieves "what you see is what you get" hands-free, silent interaction, effectively solving the control challenges of IoT devices under conditions of hand-occupancy and environmental noise interference, and possesses the advantages of high robustness and low cognitive load.

This invention provides a real-time interactive method and system for a brain-controlled rehabilitation robot based on a brain-computer interface. It synchronously acquires the user's electroencephalogram (EEG) signals and records the steady-state visual evoked potential (SSVEP) stimulation frequencies associated with different rehabilitation tasks. The system then processes the EEG signals in parallel to extract features: extracting motor imagery-related frequency band signals, obtaining tangent space feature vectors by calculating the Riemann covariance matrix, and calculating the spectral Shannon entropy; calculating response features for each SSVEP stimulation frequency; constructing a composite feature vector, inputting the vector into an intention classification model, and outputting preliminary decoding results including candidate motor intentions and confidence levels; setting a rejection threshold; if the highest confidence level is below the threshold, it is determined as an invalid instruction and feature extraction is repeated; if it is above the threshold, the corresponding candidate intention is determined as a control instruction; inputting the control instruction confidence level, SSVEP response features, and spectral Shannon entropy into a dynamic mapping model to calculate motion parameters and drive the rehabilitation robot to perform actions.

The application discloses a training method of a single-time spatial-temporal filter based on a periodic characteristic and a discriminant analysis method, and comprises the following steps: collecting original steady-state visual evoked potentials of a subject when a video frequency is f n ; pre-processing the original steady-state visual evoked potentials to obtain a sub-band; dividing the sub-band according to a length to obtain a number of data segments with the length, and the value of the length increases with the increase of the frequency f n ; averaging the data segments to obtain, and connecting multiple to obtain ; expanding and enhancing to obtain ; projecting onto a subspace spanned by an ideal reference signal to obtain ; re-enhancing to obtain ; calculating an inter-class difference matrix and an intra-class difference matrix; calculating a scatter matrix and ; obtaining a projection subspace capable of effectively classifying all classes by using generalized eigenvalue decomposition, and obtaining the spatial-temporal filter according to the projection subspace. The application achieves the technical effect of reducing the training cost of the SSVEP spatial-temporal filter.

The application discloses a kind of space positioning systems based on steady-state visual evoked potential.The stimulation module in the system utilizes the spatial coding strategy of four flicker arrays, the SSVEP signal induced is amplified and collected after signal pretreatment is carried out by electroencephalogram signal collection module, then the amplitude, phase, correlation coefficient of each stimulation frequency component and the ratio of the correlation coefficient corresponding to each coding stimulation frequency or phase are used as characteristic information to decode spatial information to determine the coordinates of visual fixation, finally the coordinate information is converted into instruction and given to controlled operation module, to realize visual feedback.The application can effectively solve the problem that the area of divided region and the number of target in each partition are limited in existing spatial coding, and the edge information of each partition cannot be distinguished, realizes the BCI of low visual load and accurate positioning in screen, and provides the possibility for realizing high-integration embedded BCI system.

This invention discloses a graded obstacle avoidance brain-controlled wheelchair and its control method based on steady-state visual evoked potentials (SVPs). By collecting EEG signals from the user's occipital lobe region and combining them with visual stimulation to induce steady-state visual evoked potentials, wheelchair movement commands are generated. Simultaneously, an environmental perception module acquires obstacle information in real time, and the wheelchair movement control module determines the obstacle avoidance status accordingly. Furthermore, the decoding process of the steady-state visual evoked potentials is constrained during the brain-controlled command generation stage, achieving coordinated control of obstacle avoidance warnings and brain-controlled commands. Through graded obstacle avoidance processing and corresponding feedback mechanisms, unsafe control commands are restricted at the generation stage, thereby improving the safety, stability, and applicability of the brain-controlled wheelchair in complex environments.

The application discloses a mechanical arm grabbing method based on stimulus control adaptive updating, which can be used for matching a steady-state visual evoked potential (SSVEP) based rehabilitation auxiliary brain-computer interface system for different application requirements. The method process is as follows: a mask region model for the task is obtained by training a self-built data set, target recognition and mask segmentation of the experimental scene image are carried out by using the mask region model, the classification, the bounding box and the mask of the object in the image are obtained, adaptive frequency and phase distribution are carried out based on the same, the stimulus control is updated in real time, the user induces the SSVEP signal by staring at the corresponding stimulus control of the target object, the recognition result is intuitively fed back to the user through display mask and the like after classification analysis, and the mechanical arm is controlled to assist the user to complete the grabbing and the like.

The application discloses a continuous word spelling method based on steady-state visual evoked potentials, and mainly solves the problems of low feature extraction efficiency and discontinuous spelling in the prior art.The implementation scheme is as follows: all characters in a stimulation interface are coded by using a joint frequency phase modulation paradigm; a user gazes at the characters in the stimulation interface to induce steady-state visual evoked potentials, and training signals and test signals are obtained; model parameters of a reconstructed template signal are acquired by using the training signals; steady-state visual evoked potential signals of all gaze stages in the test signals are intercepted; feature extraction and identification are performed on the steady-state visual evoked potential signal segments of all gaze stages by using the template signal; and character sequences in the identification results are combined into words to complete continuous word spelling input.The application improves the feature extraction efficiency and identification accuracy of the steady-state visual evoked potential signals, improves the continuity of spelling and the information transmission rate, and can be used for word spelling in a human-computer interaction process.