Adaptive LDA classifier enhances real-time control of an EEG Brain-computer interface for imagined-speech decoding

Abstract

Brain-Computer Interfaces (BCI) aim to establish a pathway between the brain and an external device without the involvement of the motor system, relying exclusively on neural signals. Such systems have the potential to provide a means of communication for patients who have lost the ability to speak due to a neurological disorder. Traditional methodologies for decoding imagined speech directly from brain signals often deploy static classifiers, that is, decoders that are computed once at the beginning of the experiment and that remain unchanged throughout the BCI use. However, this approach might be inadequate in effectively handling the non-stationary nature of electroencephalography (EEG) signals and the learning that accompanies BCI use as parameters are expected to change, all the more in a real-time setting. To address this limitation, we have developed an adaptive classifier that updates its parameters based on the incoming data in real time. We first identified optimal parameters (the update coefficient, UC) to be used in an adaptive Linear Discriminant Analysis (LDA) classifier, using a previously recorded EEG dataset, acquired while healthy participants controlled a binary BCI based on imagined syllable decoding. We subsequently tested the effectiveness of this optimization in a real-time BCI-control setting. Twenty healthy participants performed two BCI-control sessions based on the imagery of two syllables, using a static LDA and the other the adaptive LDA classifier, in randomized order. In this real-time BCI-control task, the adaptive classifier led to better performances than the static one. Furthermore, the optimal parameters for the adaptive classifier were closely aligned in both datasets acquired with the same syllable imagery task. These findings highlight the effectiveness and re-liability of adaptive LDA classifiers for real-time imagined speech decoding, and its interest for non-invasive EEG-based BCI notably characterized by low decoding accuracies.