Artificial Intelligence + Machine Learning = Deep Learning EEG

How can we apply AI and Machine Learning to EEG data? There is evidence that EEG characteristics can be used as an indication (a biomarker) of some diseases. For example, in a project funded by The Michael J. Fox Foundation, our findings indicate that there are significant differences in the EEG data of different RBD patients compared to healthy populations. More specifically, RBD subjects as a group had larger power in the frontal EEG electrodes than healthy subjects. Again taken as a group. Therefore, there is statistical significance in the difference between one group and the other. 

However, if we want to use this as a means for diagnosis, we need to take into account that diagnostic decisions are made on individuals, not on groups. For this to happen we need a decision system. We would input the data of a particular individual subject. Then we would get an answer on whether this individual is likely to develop, for instance, a neurodegenerative disease. Here is where Machine Learning and Deep Learning come into play.

For more information about BCI/EEG press here.

NEURABLE - Looking for inspired minds to work with BCIs

Neurable wants to create a world where people live without limitations by enabling 3-D control of software and devices through brain activity in real-time enabling people to play games, operate toys and drive a full-sized car using their brain activity.

They are also looking for a Machine Learning Scientist/Researcher with a salary of $100.000/year.

If you are looking to create new possibilities and unlocks the true potential of existing non-invasive BCIs press here. 

Identifying Stable Patterns over Time for Emotion Recognition from EEG

Wei-Long Zheng, Jia-Yi Zhu, and Bao-Liang Lu, from Shanghai Jiao Tong University, recently published the paper "Identifying Stable Patterns over Time for Emotion Recognition from EEG" using a machine learning approach.

(...), we focus on identifying EEG stability in emotion recognition. (...) The experimental results indicate that stable patterns exhibit consistency across sessions; the lateral temporal areas activate more for positive emotion than negative one in beta and gamma bands; the neural patterns of neutral emotion have higher alpha responses at parietal and occipital sites; and for negative emotion, the neural patterns have significant higher delta responses at parietal and occipital sites and higher gamma responses at prefrontal sites. The performance of our emotion recognition system shows that the neural patterns are relatively stable within and between sessions.

For more information about BCI/EEG press here.