Within my new project devoted to brainwaves reading using OpenBCI, let me guide you through one of such exercises.
The motivation for this project is to research and learn the BCI technology with applying Machine Learning algorithms to estimate the complexity of creating systems of communications between human and machine or in between humans with machine as intermediaries, using brain activity and BCI devices.
It was decided to train neural network model to recognize two user’s states while user performs two different but mentally similar activities: reading and writing. Reading and writing activities were performed in almost identic environments but within various 24 hours ranges. User was reading the same book and writing at the same desk in same light conditions and pose.
Data collection session consisted of two phases: 1) EEG setup (mounting, connecting to interface, checking); 2) Recording while performing activity (when user reads or writes, EEG is activated and recording signals to file).
The data analysis and model training were performed on pre-collected datasets from EEG device – Ultracortex “Mark IV” EEG Headset (8 channels).
The idea of recognizing ‘reading’and ‘writing’states was picked as simple task to train and test the model for the purpose of this project.
However, in course of research it was identified that those two states may not significantly differ from each other. First of all, when person performs routine exercises it is very hard to capture expressive signal patterns, secondly it was noted that person while reading or writing is being mostly in idle state.
Both writing or reading activities may activate similar areas of brain. For example, when person writes not just piece of text but something that requires thinking ahead, person is planning the story and vision-processing regions in the brain become active. Same process may appear while reading thoughtfully and focused.
It was noticed that unfocused reading as well as compulsive writing does not bring any benefit for the purpose of research. To address that challenge the user’s data was collected within different time ranges. There was also control put in place to maintain user focused on specific task. At the end of the day only reliable datasets were picked for training with most confidence of being appropriate for each ‘reading’ and ‘writing’ state.
Finally, the model was able to distinguish such activities given the patterns learned. That was a good start for further research.
For details, please read the blog: slavanesterov.com