Decoding Imagined Speech Using Wearable EEG Headset For a Single Subject

The ability to decode imagined speech provides further insights in the neuroscience of speech production. One of the prime use case is in catering to the communication needs of speech impaired post stroke patients who are in locked-in state. The proposed work attempts to approach the challenging task of decoding imagined speech using EEG data of a single subject collected from Emotiv Epoc +, a low-cost saline-electrode designed wearable consumer headset. The research problem focuses on two primitive forms of imagined speech activity that includes phonemes and words. For phonemes, data corresponding to 16 imagined phonemes were collected, and for words, data corresponding to two imagined words Yes and No, were collected. 11 distinct time domain features and four frequency domain parameters were extracted from the temporal windowed EEG signal forming feature-set#1, while level-4 discrete wavelet transformation (DWT) coefficients forms feature-set#2. All features from set#1, and set#2 forms feature-set#3. These feature vectors were employed to characterise different combinations of pairwise phoneme classification, presence of bilabial, fricative, nasal phonemes, multi-class phoneme, vowel classification, and binary word classification individually. Random Forest (RF) and K-Nearest Neighbour (KNN) classifiers were applied to measure the classification accuracy. Principal Component Analysis (PCA) was also used to reduce the dimensionality of the data. Electrodes and features important for classification were identified by analysing the trained models. Results were compared to the previously reported works and were found to have higher or comparable accuracy despite using consumer grade headset with fewer electrodes. The work offers insights into imagined speech synthesis using a feasible and domestic grade device, which is significant in the development of low cost speech rehabilitation and treatment methods.