Spoken and Inner Speech-related EEG Connectivity in Different Spatial Direction

• Published in: Biomedical signal processing and control 2022-01, Vol.71, p.103224, Article 103224
• Main Authors: Kiroy, V.N., Bakhtin, O.M., Krivko, E.M., Lazurenko, D.M., Aslanyan, E.V., Shaposhnikov, D.G., Shcherban, I.V.
• Format: Article
• Language: English
• Subjects: Brain-computer interfaces; Coherence; EEG; Gamma activity; Inner speech
• ISSN: 1746-8094; 1746-8108
• DOI: 10.1016/j.bspc.2021.103224

•The utterance of imagined words denoting directions in space by right-handed people was accompanied by the formation of specific spatial coherence patterns in the left-brain hemisphere.•The differences in coherence values detected in the process of imagined speech within the right hemisphere, associated with a wider frequency range (beta-gamma) were observed.•The right hemisphere is involved in analyzing the context of an utterance to build a general idea, generates visual models of words and forms verbal intentions.•Statistically significant differences in imagined speech-related brain signals prove the promising use of EEG patterns associated with inner speech for the development of BCIs and neural control systems. Although a significant number of studies have been devoted to the investigation of the electrographic correlates and neurophysiological mechanisms of spoken and inner (imagined) speech, there is a question on which EEG characteristics reflect its content. Considering that speech is a complex cognitive process which requires coordinated activity of a number of cortical structures of the large hemispheres, the EEG coherence values were studied. The values were recorded from 14 channels of 10 young men in the process of real verbalization (spoken speech) and during pronunciation of imagined words designating directions in space (up, down, right, left, forward, backward). It was shown that the level of EEG coherence is generally higher for real verbalization, most significantly at gamma-2-rhythm frequencies (55–70 Hz). Spatial coherence patterns specific to a number of words are formed in the left cerebral hemisphere during imagined utterance of words at gamma-2 frequencies. The application of machine learning and neural network classification has demonstrated a significant similarity of the generated spatial coherent patterns of spoken and inner (imagined) speech. The Multi-layer Perceptron (MLP) neural network classification method has shown the accuracy of word detection in the imagined speech according to brain activity patterns up to 49–61% for 3 classes and 33–40% for 7 classes, with a random recognition rate of 33,3 and 14,2% respectively. The latter indicates a promising application of coherence values and imagined speech denoting directions in space for the development of Brain-computer interfaces (BCIs).