# A Novel Approach to EEG Speech Activity Detection with Visual Stimuli and Mobile BCI
## 6 Discussion and Conclusions  
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. Up to now, 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. The main \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Subject Numbers** & **Subject Number** & **Precision** & **Recall** & **Accuracy** & **F1 Score** \\
**Model Training** & **Model Testing** & **[\%]** & **[\%]** & **[\%]** & **[\%]** \\ \hline \hline
1, 2, 3 & 4 & 69.61 & 92.67 & 73.71 & 79.50 \\ \hline
2, 3, 4 & 1 & 60.14 & 95.73 & 62.14 & 73.87 \\ \hline
1, 3, 4 & 2 & 61.70 & 62.81 & 57.48 & 62.25 \\ \hline
1, 2, 4 & 3 & 70.37 & 86.37 & 72.56 & 77.56 \\ \hline Average & & 65.46 & 84.40 & 66.47 & 73.30 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Cross-session model. argument against the use of such devices in the real-life conditions was based around the potential problems associated with the insufficient continuous connection for all channels, lower sample rate or difficult data transmission via wireless protocol. Our research has demonstrated good accuracy of such technology based our experiment which was focused on the speech detection through EEG signals using Feed-Forward Neural Network. Our experimental work was divided into two main parts. The first part of the experiment was focused on the generation of the Neural Network models for individual subjects. The detection results for our EEG database were very positive for our research with an average F1 score of 86.8%. Based on the above results, we can see an improvement in the speech detection system in comparison with the previous experiment [9], which achieved F1 score of 77% for the single-model. In the second part the data sets were set up for cross-subject speech detection. The cross-subject detection was considered to be an important step in our research. In this case, we expected that the combination of the brain signals from the different subjects will show a significant degradation in the accuracy as well as a higher complexity of the model generalisation. We assessed cross-subject speech detection according to the type of stimulus. EEG data were recorded in two sessions. In the first session the data were EEG data were acquired on the subjects who were tasked to pronounce the names of the colours displayed on the screen in front of them. In the second session, the subjects read the displayed text of the colours. Following the data acquisition, a comparison was carried out of the effect of these two types of visual stimuli on the speech detection. Nevertheless, the best result of speech detection was demonstrated on the data set from the reading session (F1 score of 83.69%, Table 3), the comparison of average results from all models showed that different stimuli did not have a significant effect on the detection results. The data from the colour naming session achieved a higher average F1 score just by 2.28% absolute, while the accuracy of both detections was at a very similar level. Therefore, we can assume that the speech detection system in our experiment did not have a high dependency on the nature/type of the stimulus, or that the EEG device used in the experiment did not have a sufficient resolution to distinguish between the type of the stimuli: image or text. By combining the data sets from both sessions, a larger data set was obtained which was arranged in a number of different configurations which were then further analysed in the Neural Network. Table 4 shows that the best result was achieved for the configuration data set on subjects 1, 2 and, 3 in the training model and in the testing the model from the data on subject 4. The F1 score here reached 79.5%. Although a relatively small number of subjects was involved in the acquisition of the training data sets the high accuracy of the archived results provides an incentive for further research in this area.