3 research outputs found
Two Heads are Better than One: A Bio-inspired Method for Improving Classification on EEG-ET Data
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.Comment: 6 pages, 3 figures, HCI International 2023 Poste
Analyzing Brain Activity During Learning Tasks with EEG and Machine Learning
This study aimed to analyze brain activity during various STEM activities,
exploring the feasibility of classifying between different tasks. EEG brain
data from twenty subjects engaged in five cognitive tasks were collected and
segmented into 4-second clips. Power spectral densities of brain frequency
waves were then analyzed. Testing different k-intervals with XGBoost, Random
Forest, and Bagging Classifier revealed that Random Forest performed best,
achieving a testing accuracy of 91.07% at an interval size of two. When
utilizing all four EEG channels, cognitive flexibility was most recognizable.
Task-specific classification accuracy showed the right frontal lobe excelled in
mathematical processing and planning, the left frontal lobe in cognitive
flexibility and mental flexibility, and the left temporoparietal lobe in
connections. Notably, numerous connections between frontal and temporoparietal
lobes were observed during STEM activities. This study contributes to a deeper
understanding of implementing machine learning in analyzing brain activity and
sheds light on the brain's mechanisms.Comment: 20 pages, 7 figure
Trends in Machine Learning and Electroencephalogram (EEG): A Review for Undergraduate Researchers
This paper presents a systematic literature review on Brain-Computer
Interfaces (BCIs) in the context of Machine Learning. Our focus is on
Electroencephalography (EEG) research, highlighting the latest trends as of
2023. The objective is to provide undergraduate researchers with an accessible
overview of the BCI field, covering tasks, algorithms, and datasets. By
synthesizing recent findings, our aim is to offer a fundamental understanding
of BCI research, identifying promising avenues for future investigations.Comment: 14 pages, 1 figure, HCI International 2023 Conferenc