  Student attention is an indispensable input for uncovering their goals,
intentions, and interests, which prove to be invaluable for a multitude of
research areas, ranging from psychology to interactive systems. However, most
existing methods to classify attention fail to model its complex nature. To
bridge this gap, we propose AttentioNet, a novel Convolutional Neural
Network-based approach that utilizes Electroencephalography (EEG) data to
classify attention into five states: Selective, Sustained, Divided,
Alternating, and relaxed state. We collected a dataset of 20 subjects through
standard neuropsychological tasks to elicit different attentional states. The
average across-student accuracy of our proposed model at this configuration is
92.3% (SD=3.04), which is well-suited for end-user applications. Our transfer
learning-based approach for personalizing the model to individual subjects
effectively addresses the issue of individual variability in EEG signals,
resulting in improved performance and adaptability of the model for real-world
applications. This represents a significant advancement in the field of
EEG-based classification. Experimental results demonstrate that AttentioNet
outperforms a popular EEGnet baseline (p-value < 0.05) in both
subject-independent and subject-dependent settings, confirming the
effectiveness of our proposed approach despite the limitations of our dataset.
These results highlight the promising potential of AttentioNet for attention
classification using EEG data.
