Classification based on Neural Connectivity Analysis in a Motor Imaginary Task

Haruo Mizutani, I Giannopulu

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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
Country/TerritoryJapan
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.

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