# A Novel Approach to EEG Speech Activity Detection with Visual Stimuli and Mobile BCI
###### 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. Keywords:electoreneighborography; mobile EEG device; speech detection; feed-forward neural network; visual stimuli +
Footnote †: journal: Medical  
speech. The best accuracy of the classifier detection was about 50%. In the work, the 4-channel Muse EEG headset and a 128 channel EGI device were used. Automatic real-time voice activity detection was examined in Reference [2]. This study was focused on magnetoencephalography (MEG) signals. Data was collected by the NeuroVAS device in a medical environment. The study offers a method of classifying speech and silence intervals using a support vector machine, which achieves an average accuracy of 88%. Further studies investigating speech detection from MEG signals [3] used the Gaussian mixture model and artificial neural network. In the experiment, the speech was detected on whole phrases. The best classification accuracy was achieved at 94.54%. From a neurological point of view, we can categorise human speech according to the role it represents. The similarities in EEG signals in the induction of various speech activities such as perception, production, and imagination of speech is the subject of an experimental study [4]. Studies reported results higher than the chance level for EEG classification using machine learning with a data set that includes 30 subjects. One of the first online classifications to covert speech from EEG signals has been described in Reference [5]. This work deals with the distinction of the mentally spoken words. The research involved 20 subjects who were tasked to mentally repeat the words \"yes\" and \"no\". Using support vector machine (SVM) classification an average accuracy of 75.9% was achieved. The use of speech activity detection from EEG is discussed in Reference [6]. The experiment deals with the improvement of speech recognition in a noisy environment using EEG signals. The data sets used for the experiment were recorded in a noisy environment, which was classified using Recurrent Neural Network. The results showed that the EEG signal can help improve the voice activity detection and can also be helpful as a predictor of the decision to talk or not. In our research, we dealt with the detection of speech that is associated with visual stimuli. The classification of the state of inactivity and the state when the subject was exposed to a visual stimulus has been described in Reference [7].