Abstract
With the ever-progressing development in the field of computational and analytical science the last decade has seen a big improvement in the accuracy of electroencephalography (EEG) technology. Studies try to examine possibilities to use high dimensional EEG data as a source for Brain to Computer Interface. Applications of EEG Brain to computer interface vary from emotion recognition, simple computer/device control, speech recognition up to Intelligent Prosthesis. Our research presented in this paper was focused on the study of the problematic speech activity detection using EEG data. The novel approach used in this research involved the use visual stimuli, such as reading and colour naming, and signals of speech activity detectable by EEG technology. Our proposed solution is based on a shallow Feed-Forward Artificial Neural Network with only 100 hidden neurons. Standard features such as signal energy, standard deviation, RMS, skewness, kurtosis were calculated from the original signal from 16 EEG electrodes. The novel approach in the field of Brain to computer interface applications was utilised to calculated additional set of features from the minimum phase signal. Our experimental results demonstrated F1 score of 86.80% and 83.69% speech detection accuracy based on the analysis of EEG signal from single subject and cross-subject models respectively. The importance of these results lies in the novel utilisation of the mobile device to record the nerve signals which can serve as the stepping stone for the transfer of Brain to computer interface technology from technology from a controlled environment to the real-life conditions.
Highlights
The interest in the field of speech detection and recognition has been on the increase through the last decade thanks to the new possibilities of its application in the technology that can improve our lives
Our research work has demonstrated the possibility of the speech detection using a novel mobile EEG device which can be utilised in the real-life conditions
There has been a widely accepted scepticism regarding the potential accuracy of the speech detection based on the EEG signal used outside the controlled environment
Summary
The interest in the field of speech detection and recognition has been on the increase through the last decade thanks to the new possibilities of its application in the technology that can improve our lives. Technologies which use other signals to detect speech processes include electromyography for recording the movement of facial and articulatory muscles, invasive electrocorticography for recording electrical activity on the surface of the brain, or electroencephalography for recording brain electrical activity from the surface of the head, and others. These technologies represent a wide range of possibilities for speech recognition and detection for the physically or mentally disadvantaged and for their use in environments where sound cannot be used. Following international publications and studies concerning electroencephalography (EEG) technology served as the background research for our own initiative
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