Decoding imagined speech: role of electrode localization and subject-specific classification

Abstract

Imagined speech refers to the internal rehearsal of speech without articulation. Decoding and classifying imagined speech assists motor nerve disabled patients to communicate their needs to their caretakers. ElectroEnchephoGraphy (EEG) signals of imagined speech can be decoded into target commands. Effective decoding requires localization of electrodes to isolate neural signals pertinent to imagined speech. In this research, imagined speech signals of 10 healthy individuals for eight utilitarian words were extracted using 21-channel EEG acquisition device. Time domain features were extracted and analyzed using Extra tree classifier (ETC) in subject-specific manner. Using the Gini index, the eight most important spatial features for imagined speech were isolated. Frequency domain features across five bands of brain waves were analyzed from these isolated spatial positions using Fast Fourier transform (FFT). Principal Component Analysis (PCA) was employed for dimensionality reduction and classification was done using ETC, Decision Tree (DT) and KNN. ETC performed well, with a mean accuracy of 88.77%. To improve classification performance, Long Short Term Memory (LSTM) model with a Sliding Window and Attention layer (LSTM-SWA) was implemented. LSTM-SWA achieved a mean accuracy of 92.02%, as it offers the advantage of interpretability by identifying relevant temporal segments of neural activity associated with imagined speech. These findings demonstrate the need to identify effective electrode positions and frequency domain features to design an Alternative and Augmentative Communication (AAC) device using imagined speech signals.