Work

In-Depth Inception–Attention Time Model: An Application for Driver Drowsiness Detection

Year: 2025

Type: article

Abstract: Drowsiness while driving is a common problem for many drivers and a significant problem in contemporary society. This study presents a method for detecting drowsiness while driving. The key finding is that six channels of EEG data are closely associated with drowsiness detection; this finding will contribute significantly to the development of new drowsiness detection systems. To process EEG data with high frequencies and large datasets, an in-depth Inception model suitable for time-series data was employed, incorporating a self-attention mechanism. This model effectively extracts the time–frequency representation of EEG data using a short-time Fourier transform and selectively learns important features by applying the self-attention mechanism within the Inception block structure. Additionally, channel-wise convolution is utilized to reduce the dimensionality of input data, and modified Inception blocks are stacked to enable more profound data representation. The model manages its complexity by adding partial sequential convolution filters and self-attention to the Inception blocks while performing complementary roles. Our method achieved high-performance drowsiness detection with an accuracy of 79.02% using only six EEG channels. The method contributes to ensuring accurate detection by minimizing information loss through the introduction of a self-attention mechanism.

Source: Electronics

DOI: 10.3390/electronics14061069

Authors: Minseop Lee, Minsu Cha, Jiyoung Woo

Institution: Soonchunhyang University

Language: English

Topic: EEG and Brain-Computer Interfaces

Subfield: Cognitive Neuroscience

Field: Neuroscience

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,896