Imagined Speech Classification Using EEG and Deep Learning

Abstract

In this paper, we propose an imagined speech-based brain wave pattern recognition using deep learning. Multiple features were extracted concurrently from eight-channel electroencephalography (EEG) signals. To obtain classifiable EEG data with fewer sensors, we placed the EEG sensors on carefully selected spots on the scalp. To decrease the dimensions and complexity of the EEG dataset and to avoid overfitting during the deep learning algorithm, we utilized the wavelet scattering transformation. A low-cost 8-channel EEG headset was used with MATLAB 2023a to acquire the EEG data. The long-short term memory recurrent neural network (LSTM-RNN) was used to decode the identified EEG signals into four audio commands: up, down, left, and right. Wavelet scattering transformation was applied to extract the most stable features by passing the EEG dataset through a series of filtration processes. Filtration was implemented for each individual command in the EEG datasets. The proposed imagined speech-based brain wave pattern recognition approach achieved a 92.50% overall classification accuracy. This accuracy is promising for designing a trustworthy imagined speech-based brain–computer interface (BCI) future real-time systems. For better evaluation of the classification performance, other metrics were considered, and we obtained 92.74%, 92.50%, and 92.62% for precision, recall, and F1-score, respectively.

Inner Speech Recognition for Mutism and Speech Disorder Using Brain-Computer Interface

“…This paper is a follow up for our previous work in [44] where we used a low-cost 8-channels EEG headset, g.tec Unicorn Hybrid Black+ [32]. Audio cues were employed for the purpose of stimulating motor imagery in this study.…”

EEG-Based Classification of Spoken Words Using Machine Learning Approaches

“…Most work on word decoding performs an intra-subject classification; in [31], they mapped the EEG signal across seven frequency bands over nine brain regions and used a deep long short-term memory (LSTM) network for inter-subject classification. Similarly, using an LSTM along with a wavelet sparsity transformation in [32], four English words were decoded. One growing area is the ability to gradually add new words to the vocabulary using incremental learning methods, similar to the approach used in [33].…”