# A Novel Approach to EEG Speech Activity Detection with Visual Stimuli and Mobile BCI
## 3 An Experimental Database  
### Materials  
Although considerable research has been done in modeling human speech detection and recognition, it is difficult to use this information to create a speech recognition model that works on a mobile EEG device. Working with mobile devices is often more difficult due to the inferior signal acquisition conditions. However, mobile EEG devices bring many benefits to end-users such as lower price, easier connection, easier to place on the head or the use of dry electrodes, to name few. In our research the database of EEG signals was recorded using the OpenBci Ultracortex Mark III EEG headset. Bluetooth signal was used to both control the mobile EEG head cap via a computer and to transfer the recorded EEG signal. The sampling frequency of 125 Hz was used to capture the brain signal via the EEG headset.  
The headset configuration consisted of 16 dry electrodes that recorded the signals in different areas of the brain. The channels used were namely Fp1, Fp2, F3, F4, F7, F8, C3, C4, T3, T4, P3, P4, T5, T6, O1, O2, according to the international 10/20 system. The decision to apply the electrode configuration to the entire head area was made based on the reasons given in Section 2. Since the research focused on the brain activity during an overt speech associated with visual stimuli it is important to capture the signals coming from areas of the brain not just the speech area. Figure 1b shows the side view of the location of the sensors use in our research and the iodine of the brain is shown. Due to the limited possibilities of positioning the electrodes on the Ultracortex EEG headset, we tried to cover the following areas: Speech areas ([PERSON]'s and [PERSON]'s area), Auditory areas, Visual areas and Motor areas. Another reason to use EEG signals coming from multiple areas is that, despite the well-known knowledge that [PERSON]'s and Wernicke's areas are located on the left hemisphere in about 95% of right-handers and 70% of left-handers [15], observations in the previous experiment [8] did not show a significant improvement for the machine learning based on the modeling with left hemisphere EEG signals. The experiment in Reference [14], showed that the classification of EEG signals from the left hemisphere shows just slightly better accuracies compared to signals from the right hemisphere signals classification.  
Our EEG headset has been modified to use soft electrodes to improve the subject's comfort. Fitted measuring electrodes are based on a flexible conductive, elastic main body with a conductive coating covering the contact area. The functionality of the electrodes with the OpenBCI headset was discussed in Reference [16]. Our solution for the connection of the electrodes to the headset resulted in the snap connectors being soldered to wires to which electrodes were connected.  
The EEG signals was recorded on four healthy, right-handed native Slovak speaking subjects. They signed an informed consent form in which they were acquainted with the purpose of the experiment and with the use and management of the personal data. The research was carried out in accordance with the Code of Ethics for employees of the Technical University in Kosice.  
During the experiment the subjects followed an experimental protocol. Their task was to sit motionlessly in a comfortable position while focusing on the screen in front of them and processing the word pronunciation. During the EEG signal recording the audio signal was also gathered, which were used for speech labels creating.