Classification based on Neural Connectivity Analysis in a Motor Imaginary Task

Haruo Mizutani, I Giannopulu

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

9 Downloads (Pure)

Abstract

This study aims at developing a classification
method based on the neural connectivity by human EEG
dataset. An experimental result of four class classification
on a motor imagery task indicates that our novel analytical
method has a good performance and yields a crossvalidation accuracy of 53.5%.
Original languageEnglish
Title of host publicationThe Proceedings of the 28th Annual Conference of the Japanese Neural Network Society
Subtitle of host publication第 28 回 日本神経回路学会全国大会 講演論文集
PublisherOkinawa Institute of Science and Technology
ChapterP2-6
Pages122-123
Number of pages2
Publication statusPublished - Oct 2018
EventThe 28th Annual Conference of the Japanese Neural Network Society - Okinawa Institute of Science and Technology (OIST)., Okinawa, Japan
Duration: 24 Oct 201827 Oct 2018
Conference number: 28th
http://jnns.org/conference/2018/index.html

Conference

ConferenceThe 28th Annual Conference of the Japanese Neural Network Society
Abbreviated titleJNNS2018
CountryJapan
CityOkinawa
Period24/10/1827/10/18
OtherThe 28th Annual Conference of the Japanese Neural Network Society (JNNS2018) will be held from October 24th to 27th at Okinawa Institute of Science and Technology (OIST).

JNNS2018 aims to provide a forum for scientists, engineers, educators, and students to discuss the latest progress and future challenges in the field of neural information processing.

This year, JNNS aims to make the conference international with all the presentations in English and world-renowned keynote lecturers, Dr. Shun-ich Amari (RIKEN Center for Brain Science) and Dr. Maneesh Sahani (Gatsby Computational Neuroscience Unit, UCL). Travel support will be provided for selected student presenters.

Internet address

Fingerprint

Imagery (Psychotherapy)

Cite this

Mizutani, H., & Giannopulu, I. (2018). Classification based on Neural Connectivity Analysis in a Motor Imaginary Task. In The Proceedings of the 28th Annual Conference of the Japanese Neural Network Society: 第 28 回 日本神経回路学会全国大会 講演論文集 (pp. 122-123). Okinawa Institute of Science and Technology .
Mizutani, Haruo ; Giannopulu, I. / Classification based on Neural Connectivity Analysis in a Motor Imaginary Task. The Proceedings of the 28th Annual Conference of the Japanese Neural Network Society: 第 28 回 日本神経回路学会全国大会 講演論文集. Okinawa Institute of Science and Technology , 2018. pp. 122-123
@inproceedings{e6aa4ca5b19c4fca887dfeccc2efac8f,
title = "Classification based on Neural Connectivity Analysis in a Motor Imaginary Task",
abstract = "This study aims at developing a classificationmethod based on the neural connectivity by human EEGdataset. An experimental result of four class classificationon a motor imagery task indicates that our novel analyticalmethod has a good performance and yields a crossvalidation accuracy of 53.5{\%}.",
author = "Haruo Mizutani and I Giannopulu",
year = "2018",
month = "10",
language = "English",
pages = "122--123",
booktitle = "The Proceedings of the 28th Annual Conference of the Japanese Neural Network Society",
publisher = "Okinawa Institute of Science and Technology",

}

Mizutani, H & Giannopulu, I 2018, Classification based on Neural Connectivity Analysis in a Motor Imaginary Task. in The Proceedings of the 28th Annual Conference of the Japanese Neural Network Society: 第 28 回 日本神経回路学会全国大会 講演論文集. Okinawa Institute of Science and Technology , pp. 122-123, The 28th Annual Conference of the Japanese Neural Network Society, Okinawa, Japan, 24/10/18.

Classification based on Neural Connectivity Analysis in a Motor Imaginary Task. / Mizutani, Haruo; Giannopulu, I.

The Proceedings of the 28th Annual Conference of the Japanese Neural Network Society: 第 28 回 日本神経回路学会全国大会 講演論文集. Okinawa Institute of Science and Technology , 2018. p. 122-123.

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

TY - GEN

T1 - Classification based on Neural Connectivity Analysis in a Motor Imaginary Task

AU - Mizutani, Haruo

AU - Giannopulu, I

PY - 2018/10

Y1 - 2018/10

N2 - This study aims at developing a classificationmethod based on the neural connectivity by human EEGdataset. An experimental result of four class classificationon a motor imagery task indicates that our novel analyticalmethod has a good performance and yields a crossvalidation accuracy of 53.5%.

AB - This study aims at developing a classificationmethod based on the neural connectivity by human EEGdataset. An experimental result of four class classificationon a motor imagery task indicates that our novel analyticalmethod has a good performance and yields a crossvalidation accuracy of 53.5%.

M3 - Conference contribution

SP - 122

EP - 123

BT - The Proceedings of the 28th Annual Conference of the Japanese Neural Network Society

PB - Okinawa Institute of Science and Technology

ER -

Mizutani H, Giannopulu I. Classification based on Neural Connectivity Analysis in a Motor Imaginary Task. In The Proceedings of the 28th Annual Conference of the Japanese Neural Network Society: 第 28 回 日本神経回路学会全国大会 講演論文集. Okinawa Institute of Science and Technology . 2018. p. 122-123