Two Heads are Better than One: A Bio-inspired Method for Improving Classification on EEG-ET Data
This work addresses noise and dynamic brain activity issues in EEG classification for BCI applications, representing an incremental advance.
The paper tackled the problem of EEG data classification for Brain Computer Interfaces by integrating feature selection and time segmentation, resulting in significant performance improvements and reduced computational complexity on the EEGEyeNet dataset.
Classifying EEG data is integral to the performance of Brain Computer Interfaces (BCI) and their applications. However, external noise often obstructs EEG data due to its biological nature and complex data collection process. Especially when dealing with classification tasks, standard EEG preprocessing approaches extract relevant events and features from the entire dataset. However, these approaches treat all relevant cognitive events equally and overlook the dynamic nature of the brain over time. In contrast, we are inspired by neuroscience studies to use a novel approach that integrates feature selection and time segmentation of EEG data. When tested on the EEGEyeNet dataset, our proposed method significantly increases the performance of Machine Learning classifiers while reducing their respective computational complexity.