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Kazuyuki Matsunaga

dblp:48/7947 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2005
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Human-computer interaction and pervasive computing
1 paper
Wearable and physiological sensing · 62% Accessibility and assistive technology · 38%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Accessibility and assistive technology
assistive technology
0.112005
Electroencephalogram-Based Control of an Electric Wheelchair · IEEE Trans. Robotics 2005
Wearable and physiological sensing
brain-computer interface
0.112005
Electroencephalogram-Based Control of an Electric Wheelchair · IEEE Trans. Robotics 2005
Wearable and physiological sensing › electroencephalography
EEG decoding
0.012005
Electroencephalogram-Based Control of an Electric Wheelchair · IEEE Trans. Robotics 2005
Wearable and physiological sensing › electroencephalography
EEG signal processing
0.012005
Electroencephalogram-Based Control of an Electric Wheelchair · IEEE Trans. Robotics 2005

Methods — techniques the papers use, named apart from their topics

recursive training algorithm · 0.1EEG signal classification · 0.1
YearPublicationVenuePosition
2005 Electroencephalogram-Based Control of an Electric Wheelchair
abstract
This paper presents a study on electroencephalogram (EEG)-based control of an electric wheelchair. The objective is to control the direction of an electric wheelchair using only EEG signals. In other words, this is an attempt to use brain signals to control mechanical devices such as wheelchairs. To achieve this goal, we have developed a recursive training algorithm to generate recognition patterns from EEG signals. Our experimental results demonstrate the utility of the proposed recursive training algorithm and the viability of accomplishing direction control of an electric wheelchair by only EEG signals.
Kazuo Tanaka, Kazuyuki Matsunaga, Hua O. Wang
IEEE Trans. Robotics2