zoraizq / brain_mas_classifier

Mental Attention State Classification for the Human Brain using EEG-based BCI data

GA-ANFIS and CRNN for Mental Attention State Classification using Passive BCI-based EEG data

By using electroencephalography (EEG) based BCI intrinsic or passive activity data self-generated by specified individuals under simulation or obtained live [3], we aim to detect and classify the current mental attention state of an individual into several categories of states, the most prominent being - focused, unfocused (may also be described as lost-in-thought or mind-wandering) and drowsy (or sleep). Our approach uses a hybrid neuro-genetic fuzzy system optimized and trained on a select number of features and channels extracted from the data as well as a shallow convolutional and convolutional recurrent neural network trained on the raw EEG signal data to predict human mental attention state with signals of trial lengths as short as 6 seconds.

View the complete project report here.

Dataset

• EEG data for Mental Attention State Detection. 2021. Retrieved 15 March 2021, from https://www.kaggle.com/inancigdem/eeg-data-for-mental-attention-state-detection

Collaborators

• Zoraiz Qureshi, BS Computer Science, Lahore University of Management Sciences
• Syed Ali Hassan Bukhari, BS Computer Science, Lahore University of Management Sciences
• Dr. Mian Muhammad Awais, Professor, Lahore University of Management Sciences