Brain-Computer Interface for Stroke Rehabilitation
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Solution Overview
Problem
Current methods for treating stroke patients with severe hemiplegia, such as physiotherapy and functional electrical stimulation (FES) therapy, face challenges in accurately determining patient intent and timing of movement, leading to uncertain involvement of the central nervous system and potential suboptimal neuromotor rehabilitation.
Innovation Solution
A method involving brain electrical signal analysis through temporo-spectral decomposition and brain-computer interfaces (BCIs) to characterize and decode intended motor actions, enabling precise control of devices like robotic arms or FES systems to align with patient intent and optimize neuromotor rehabilitation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If therapist manually determines patient intent to trigger FES stimulation, then treatment can be applied, but certainty of patient intent and timing precision deteriorate
Solution Approach 1:
The patent replaces the manual mechanical assessment method (therapist observation and judgment) with an automated brain-computer interface system that uses electroencephalography (EEG) to detect and decode patient intent. This substitution of mechanical/subjective assessment with electronic/objective measurement directly resolves the contradiction by providing reliable, precise intent detection without requiring complex manual evaluation procedures.
Solution Approach 2:
The patent introduces an intermediary system (BCI with EEG processing) between the patient's neural intent and the FES stimulation delivery. This intermediary automatically detects neural signals, processes them through temporo-spectral decomposition and classification algorithms, and triggers stimulation at the optimal time. The intermediary resolves the contradiction by providing both high reliability in intent detection and precise timing control.
2Loss of time
If FES stimulation is delivered based on therapist assessment, then treatment can proceed, but timing precision and optimal recovery latency deteriorate
Solution Approach 1:
The patent applies preliminary action by detecting and decoding patient intent through EEG analysis before delivering FES stimulation. The system continuously monitors brain signals, performs temporo-spectral decomposition to identify intent-related patterns, and triggers stimulation at the optimal moment. This preliminary detection and processing ensures timing precision and optimal recovery latency by preparing the stimulation delivery in advance based on actual patient intent.
3Productivity
If conventional physiotherapy and occupational therapy are used, then motor recovery can be achieved, but recovery plateaus after six months
Solution Approach 1:
The patent implements feedback by using BCI technology to detect patient intent through EEG signals and adjust FES stimulation delivery in real-time. The system provides closed-loop feedback where neural activity is continuously monitored, processed through classification algorithms, and used to control stimulation timing and intensity. This feedback mechanism extends effective therapy duration beyond the six-month plateau by maintaining neuroplasticity through precisely timed, intent-based stimulation.
Data Source
AI summary
A method for characterizing a brain electrical signal comprising forming a temporo-spectral decomposition of the signal to form a plurality of time resolved frequency signal values, associating each instance of the signal value with a predetermined function approximating a neurological signal to form a table of coefficients collectively representative of the brain electrical signal.