Deep Neural Network BCI Decoding for Recalibration-Free FES Control
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Solution Overview
Problem
Current brain-computer interface (BCI) systems face challenges in achieving high accuracy, fast response times, and minimizing daily recalibration needs, which are essential for clinical viability and user acceptance, particularly in restoring hand and arm function for individuals with paralysis.
Innovation Solution
A BCI system incorporating a multichannel stimulator and a decoder with a deep neural network that includes a long short-term memory (LSTM) layer and a convolutional layer, capable of unsupervised updating using neural signals, allowing for the prediction of movement intentions and control of functional electrical stimulation (FES) devices without requiring explicit user intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional BCI decoders are used, then the system requires daily recalibration to maintain accuracy, but this increases setup time and reduces ease of operation
Solution Approach 1:
The decoder performs self-updating by automatically adapting to changes in neural signals over time without requiring user intervention or daily recalibration. The system uses unsupervised learning to continuously refine its decoding performance, making the recalibration process autonomous and eliminating the burden on users.
Solution Approach 2:
The system performs preliminary adaptation during an initial training period, learning the user's neural patterns in advance. This preliminary learning phase enables the decoder to maintain high accuracy over extended periods without requiring subsequent recalibration, as the system has already adapted to the user's specific neural characteristics.
2Measurement precision
If complex deep neural networks are implemented, then decoding accuracy improves, but computational complexity and processing time increase
Solution Approach 1:
The deep neural network is segmented into distinct functional layers: convolutional layers for extracting spatial features from neural signals, recurrent layers for capturing temporal dependencies, and output layers for generating movement predictions. This segmentation allows each layer to specialize in specific processing tasks, improving overall efficiency while maintaining high accuracy.
Solution Approach 2:
The decoder architecture is designed to be dynamic, adapting its processing based on the characteristics of the input neural signals. The system can adjust the depth and complexity of network processing in real-time, using simpler processing paths when sufficient accuracy is achieved and engaging more complex processing only when needed, thereby optimizing computational efficiency.
3Adaptability or versatility
If more neural signal channels are processed, then the number of decodable functions increases, but system complexity and data processing requirements increase
Solution Approach 1:
The deep neural network decoder is designed as a universal system that can decode multiple types of movement intentions from the same neural signal input. By training the network on diverse movement datasets during the preliminary learning phase, the system acquires the capability to control various assistive devices and perform multiple functions without requiring separate decoders for each task.
Solution Approach 2:
The system controls the number of processed neural signal channels and the complexity of decoding as adjustable parameters. During initial setup, the system can optimize the number of channels and processing depth based on the user's specific needs and the complexity of required functions, allowing flexible adaptation between simplicity and versatility depending on the application context.
Data Source
AI summary
A brain-computer interface (BCI) includes a multichannel stimulator and a decoder. The multichannel stimulator is operatively connected to deliver stimulation pulses to a functional electrical stimulation (FES) device to control delivery of FES to an anatomical region. The decoder is operatively connected to receive at least one neural signal from at least one electrode operatively connected with a motor cortex. The decoder controls the multichannel stimulator based on the received at least one neural signal. The decoder comprises a computer programmed to process the received at least one neural signal using a deep neural network. The decoder may include a long short-term memory (LSTM) layer outputting to a convolutional layer in turn outputting to at least one fully connected neural network layer. The decoder may be updated by unsupervised updating. The decoder may be extended to include additional functions by transfer learning.