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Compact convolutional neural network (CNN) based on SincNet for end-to-end motor imagery decoding and analysis

Izzuddin Tarmizi Ahmad, Safri Norlaili Mat, Othman Mohd Afzan

Biocybernetics and Biomedical Engineering

2021

Vol. 41, no. 4

1629--1645

In the field of human-computer interaction, the detection, extraction and classification of the electroencephalogram (EEG) spectral and spatial features are crucial towards developing a practical and robust non-invasive EEG-based brain-computer interface. Recently, due to the popularity of end-to-end deep learning, the applicability of algorithms such as convolutional neural networks (CNN) has been explored to achieve the mentioned tasks. This paper presents an improved and compact CNN algorithm for motor imagery decoding based on the adaptation of SincNet, which was initially developed for speaker recognition task from the raw audio input. Such adaptation allows for a compact end-to-end neural network with state-of-the-art (SOTA) performances and enables network interpretability for neurophysiological validation in cortical rhythms and spatial analysis. In order to validate the performance of proposed algorithms, two datasets were used; the first is the publicly available BCI Competition IV dataset 2a, which was often used as a benchmark in validating motor imagery classification algorithms, and the second is a dataset consists of primary data initially collected to study the difference between motor imagery and mental-task associated motor imagery BCI and was used to test the plausibility of the proposed algorithm in highlighting the differences in terms of cortical rhythms. Competitive decoding performance was achieved in both datasets in comparisons with SOTA CNN models, albeit with the lowest number of trainable parameters. In addition, it was shown that the proposed architecture performs a cleaner band-pass, highlighting the necessary frequency bands that were crucial and neurophysiologically plausible in solving the classification tasks.

Robotic orthosis compared to virtual hand for Brain–Computer Interface feedback

Cantillo-Negrete Jessica, Carino-Escobar Ruben I., Carrillo-Mora Paul, Barraza-Madrigal José A., Arias-Carrión Oscar

Biocybernetics and Biomedical Engineering

2019

Vol. 39, no. 2

263--272

Brain–Computer Interfaces (BCI) allow the control of external devices by decoding the users' intentions from their central nervous system. Feedback, one of the main elements of a closed- loop BCI, is used to enhance the user's performance. The present work aimed to compare the effect of two different feedback sources; congruent anatomical visual hand representation and passive hand movement on BCI performance and cortical activations. Electroencephalography of 12 healthy right-handed subjects was recorded to set a BCI activated by right-hand motor imagery. Afterward, the subjects were asked to control the system by imagining the movement. The system provided either visual feedback, shown on a computer screen or kinesthetic feedback, provided by a robotic hand orthosis. Differences in performance and cortical activations were assessed, using classification accuracy and event-related desynchronization/synchronization in μ and β bands, respectively. Performance was significantly better with kinesthetic feedback as it allowed for higher correct classification of motor imagery. Cortical activations in the ipsilateral central channel in μ were different between the two feedback modalities. Our results imply that healthy subjects can achieve a greater degree of control using a motor imagery-based BCI with kinesthetic feedback than with anatomically congruent visual feedback. Furthermore, cortical activation differences show that kinesthetic feedback seems to elicit higher recruitment of sensorimotor cortex brain cells, which probably reflects enhanced local information modulation related to fine motor processing. Therefore, kinesthetic feedback provided by a robotic orthosis could be a more suitable feedback strategy for BCI systems designed for neuromodulation and neurorehabilitation.