Presentation
Towards Accurate, Real-Time, and Energy-Efficient Attention Monitoring
Descriptionttention monitoring is crucial in various fields,
especially those involving prolonged periods of passive
observation. Electroencephalography (EEG) offers a cost-
effective, portable, and non-invasive solution; however,
the signal's intrinsic complexity necessitate advanced
analysis techniques. This research proposes a novel
approach for accurate attention classification. We selected
a representative channel and applied Recursive Feature
Elimination (RFE) to identify the most discriminating
features to enhance detection accuracy and minimize energy
consumption. On a public dataset, our optimized XGBoost
algorithm achieved 98.29% and 94.25% accuracy for binary
and three-class attention classification, respectively. The
refined feature set reduces execution time by 9%, memory
usage by 38%, and energy consumption by 8% on an Intel
i9-13900 platform. On a Raspberry Pi 5 the improvements
are 15%, 14%, and 14% respectively. By addressing these
challenges our approach facilitates adoption of wearable
devices for attention monitoring in various settings.
Index Terms—EEG signal, attention detection, machine
learning, feature engineering, wearable devices.
especially those involving prolonged periods of passive
observation. Electroencephalography (EEG) offers a cost-
effective, portable, and non-invasive solution; however,
the signal's intrinsic complexity necessitate advanced
analysis techniques. This research proposes a novel
approach for accurate attention classification. We selected
a representative channel and applied Recursive Feature
Elimination (RFE) to identify the most discriminating
features to enhance detection accuracy and minimize energy
consumption. On a public dataset, our optimized XGBoost
algorithm achieved 98.29% and 94.25% accuracy for binary
and three-class attention classification, respectively. The
refined feature set reduces execution time by 9%, memory
usage by 38%, and energy consumption by 8% on an Intel
i9-13900 platform. On a Raspberry Pi 5 the improvements
are 15%, 14%, and 14% respectively. By addressing these
challenges our approach facilitates adoption of wearable
devices for attention monitoring in various settings.
Index Terms—EEG signal, attention detection, machine
learning, feature engineering, wearable devices.
Event Type
Networking
Work-in-Progress Poster
TimeMonday, June 236:00pm - 7:00pm PDT
LocationLevel 2 Lobby
Similar Presentations