NDLI: Letter composition task classification using NIRS and neural network

Content Provider	IEEE Xplore Digital Library
Author	Komatsuzaki, R. Takahashi, S. Nakamura, H. Tsunashima, H.
Copyright Year	2011
Description	Author affiliation: Department of Mechanical Engineering, College of Industrial Technology, Nihon University, Chiba, Japan (Tsunashima, H.) \|\| Department of Electronics and Computer Science, College of Science and Technology, Nihon University, Chiba, Japan (Komatsuzaki, R.; Takahashi, S.; Nakamura, H.)
Abstract	We describe letter composition task classification using near-infrared spectroscopy (NIRS) and our proposed neural network model called Neo-ISRM. Brain-Computer Interfaces (BCIs) are a new paradigm for communication between humans and machines and can provide a communication means for people with severe physical disabilities. Our objective is to develop a BCI system that can handle various intentions of users. A BCI should recognize various kinds of commands for controlling a machine. The number of commands corresponds to the number of categories into which a classifier can classify. In this paper, we describe the classification of NIRS data acquired while a subject performed letter composition tasks involving both category fluency and letter fluency using Neo-ISRM. For classifying tasks, two different areas were selected: the whole area (frontal and temporal lobes of the left hemisphere) having 22 data channels, and a local area having 9 data channels. The results for the local area showed that category fluency and letter fluency tasks can be classified from the NIRS signals by using our neural classifier Neo-ISRM. The results show that it is possible to increase the number of categories into which a BCI system can classify.
Starting Page	352
Ending Page	354
File Size	547057
Page Count	3
File Format	PDF
ISBN	9781457708350
ISSN	20937121
e-ISBN	9788993215038
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-10-26
Publisher Place	Korea (South)
Access Restriction	Subscribed
Rights Holder	ICROS
Subject Keyword	Temporal lobe Microprocessors category fluency letter fluency brain-computer interface Computer architecture NIRS Brain modeling Educational institutions letter composition task neural network Biological neural networks
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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