abstract
- Attentional lapses while individuals are engaged in activities can have critical effects on their performance. EEG sensors offer the potential to monitor brain activity and detect such lapses in-situ. However, advances in automatic detection are limited, notably due to the scarcity of validated EEG data for training. In this work, we explore the design space of lapses-of-attention detectors using EEG signals, framing it as a binary classification problem. We introduce an EEG dataset with two validated attention levels acquired through a controlled experiment (N = 24) involving reading tasks with and without auditory distractions. We evaluated fifteen detectors using three different EEG feature extraction techniques, and five classifier models. Models using filterbank-CSP features yielded the highest median per-participant detection accuracy of 96%. Limited-resource analyses further indicate the Beta frequency band is the most informative for attention detection, and highlight detection can be achieved with only four EEG channels. Taken together, our findings inform on the feasibility and design of automatic attention detection for brain-computer interfaces utilizing simpler EEG devices.