Toward asynchronous EEG-based BCI: Detecting imagined words segments in continuous EEG signals

TL;DR

This paper explores five feature extraction methods to detect imagined words in continuous EEG signals, aiming to develop an asynchronous EEG-based BCI that can identify when a user starts imagining speech.

Contribution

It introduces and evaluates five novel feature extraction techniques for detecting imagined speech segments in EEG signals, advancing asynchronous BCI development.

Findings

Highest F1 scores of 0.79 across datasets

Wavelet and empirical mode decomposition methods performed best

Results support automated segmentation for imagined speech BCI

Abstract

An asynchronous Brain--Computer Interface (BCI) based on imagined speech is a tool that allows to control an external device or to emit a message at the moment the user desires to by decoding EEG signals of imagined speech. In order to correctly implement these types of BCI, we must be able to detect from a continuous signal, when the subject starts to imagine words. In this work, five methods of feature extraction based on wavelet decomposition, empirical mode decomposition, frequency energies, fractal dimension and chaos theory features are presented to solve the task of detecting imagined words segments from continuous EEG signals as a preliminary study for a latter implementation of an asynchronous BCI based on imagined speech. These methods are tested in three datasets using four different classifiers and the higher F1 scores obtained are 0.73, 0.79, and 0.68 for each dataset, respectively. This results are promising to build a system that automatizes the segmentation of imagined words segments for latter classification.