Ventral and Dorsal Stream EEG Channels: Key Features for EEG-Based Object Recognition and Identification.

Leong, D; Do, TT-T; Lin, C-T

Ventral and Dorsal Stream EEG Channels: Key Features for EEG-Based Object Recognition and Identification.

Leong, D Do, TT-T Lin, C-T

Permalink

Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Trans Neural Syst Rehabil Eng, 2023, 31, pp. 4862-4870
Issue Date:: 2023

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download Published versionAdobe PDF (3.34 MB)

View on publisher's site

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Leong, D
dc.contributor.author	Do, TT-T
dc.contributor.author	Lin, C-T
dc.date.accessioned	2024-03-05T03:57:44Z
dc.date.available	2024-03-05T03:57:44Z
dc.date.issued	2023
dc.identifier.citation	IEEE Trans Neural Syst Rehabil Eng, 2023, 31, pp. 4862-4870
dc.identifier.issn	1534-4320
dc.identifier.issn	1558-0210
dc.identifier.uri	http://hdl.handle.net/10453/176130
dc.description.abstract	Object recognition and object identification are multifaceted cognitive operations that require various brain regions to synthesize and process information. Prior research has evidenced the activity of both visual and temporal cortices during these tasks. Notwithstanding their similarities, object recognition and identification are recognized as separate brain functions. Drawing from the two-stream hypothesis, our investigation aims to understand whether the channels within the ventral and dorsal streams contain pertinent information for effective model learning regarding object recognition and identification tasks. By utilizing the data we collected during the object recognition and identification experiment, we scrutinized EEGNet models, trained using channels that replicate the two-stream hypothesis pathways, against a model trained using all available channels. The outcomes reveal that the model trained solely using the temporal region delivered a high accuracy level in classifying four distinct object categories. Specifically, the object recognition and object identification models achieved an accuracy of 89% and 85%, respectively. By incorporating the channels that mimic the ventral stream, the model's accuracy was further improved, with the object recognition model and object identification model achieving an accuracy of 95% and 94%, respectively. Furthermore, the Grad-CAM result of the trained models revealed a significant contribution from the ventral and dorsal stream channels toward the training of the EEGNet model. The aim of our study is to pinpoint the optimal channel configuration that provides a swift and accurate brain-computer interface system for object recognition and identification.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation	http://purl.org/au-research/grants/arc/DP180100656
dc.relation	http://purl.org/au-research/grants/arc/DP210101093
dc.relation.ispartof	IEEE Trans Neural Syst Rehabil Eng
dc.relation.isbasedon	10.1109/TNSRE.2023.3339698
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0903 Biomedical Engineering, 0906 Electrical and Electronic Engineering
dc.subject.classification	Biomedical Engineering
dc.subject.classification	4003 Biomedical engineering
dc.subject.classification	4007 Control engineering, mechatronics and robotics
dc.subject.mesh	Humans
dc.subject.mesh	Pattern Recognition, Visual
dc.subject.mesh	Visual Perception
dc.subject.mesh	Brain
dc.subject.mesh	Temporal Lobe
dc.subject.mesh	Magnetic Resonance Imaging
dc.subject.mesh	Electroencephalography
dc.subject.mesh	Brain Mapping
dc.subject.mesh	Brain
dc.subject.mesh	Temporal Lobe
dc.subject.mesh	Humans
dc.subject.mesh	Magnetic Resonance Imaging
dc.subject.mesh	Electroencephalography
dc.subject.mesh	Brain Mapping
dc.subject.mesh	Pattern Recognition, Visual
dc.subject.mesh	Visual Perception
dc.subject.mesh	Humans
dc.subject.mesh	Pattern Recognition, Visual
dc.subject.mesh	Visual Perception
dc.subject.mesh	Brain
dc.subject.mesh	Temporal Lobe
dc.subject.mesh	Magnetic Resonance Imaging
dc.subject.mesh	Electroencephalography
dc.subject.mesh	Brain Mapping
dc.title	Ventral and Dorsal Stream EEG Channels: Key Features for EEG-Based Object Recognition and Identification.
dc.type	Journal Article
utslib.citation.volume	31
utslib.location.activity	United States
utslib.for	0903 Biomedical Engineering
utslib.for	0906 Electrical and Electronic Engineering
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	open_access	*
dc.date.updated	2024-03-05T03:57:38Z
pubs.publication-status	Published
pubs.volume	31

Abstract:

Object recognition and object identification are multifaceted cognitive operations that require various brain regions to synthesize and process information. Prior research has evidenced the activity of both visual and temporal cortices during these tasks. Notwithstanding their similarities, object recognition and identification are recognized as separate brain functions. Drawing from the two-stream hypothesis, our investigation aims to understand whether the channels within the ventral and dorsal streams contain pertinent information for effective model learning regarding object recognition and identification tasks. By utilizing the data we collected during the object recognition and identification experiment, we scrutinized EEGNet models, trained using channels that replicate the two-stream hypothesis pathways, against a model trained using all available channels. The outcomes reveal that the model trained solely using the temporal region delivered a high accuracy level in classifying four distinct object categories. Specifically, the object recognition and object identification models achieved an accuracy of 89% and 85%, respectively. By incorporating the channels that mimic the ventral stream, the model's accuracy was further improved, with the object recognition model and object identification model achieving an accuracy of 95% and 94%, respectively. Furthermore, the Grad-CAM result of the trained models revealed a significant contribution from the ventral and dorsal stream channels toward the training of the EEGNet model. The aim of our study is to pinpoint the optimal channel configuration that provides a swift and accurate brain-computer interface system for object recognition and identification.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/176130