Single-Trail EEG Processing for Brain Computer Interface

被引：0

作者：

Funase, A. ^{[1
]}

Nakatani, H. ^{[2
]}

Mouri, M. ^{[1
]}

Yagi, T. ^{[3
,4
]}

Cichocki, A. ^{[2
]}

Takumi, I. ^{[1
]}

机构：

[1] Nagoya Inst Technol, Grad Sch Engn, Showa Ku, Nagoya, Aichi, Japan

[2] Brain Sci Inst, RIKEN, Wako, Saitama, Japan

[3] Tokyo Inst Technol, Grad Sch Informat Sci & Engn, Tokyo, Japan

[4] RIKEN, Bio Mimet Control Res Ctr, Nagoya, Aichi, Japan

来源：

WORLD CONGRESS ON MEDICAL PHYSICS AND BIOMEDICAL ENGINEERING 2006, VOL 14, PTS 1-6 | 2007年 / 14卷

关键词：

brain computer interface; saccadic eye movement; independent component analysis;

D O I：

暂无

中图分类号：

R318 [生物医学工程];

学科分类号：

0831 ;

摘要：

We have been researching relationship between saccadic eye movements and EEG signals before saccadic eye movements in order to develop the Brain-Computer Interface (BCI). In our previous studies, saccade-related EEG signals were obtained with ensemble averaging. However, the ensemble averaging is not suitable for BCIs because ensemble averaging is not can not process EEG signals in real-time. In order to overcome this problem, an independent component analysis (ICA) is applied to processing on saccade-related EEG signals. We have already detected saccade-related EEG signals by ensemble averaging and saccade-related independent components (ICs) by independent component analysis (ICA). However, features of saccade-related EEG signals and saccade-related ICs were not compared. In this study, saccade-related EEG signals and saccade-related ICs in visually and auditorily guided saccade task are compared in the point of the latency between starting time of a saccade and time when a saccade-related EEG signal or an IC has maximum value and in the point of the peak scale where a saccade-related EEG signal or an IC has maximum value.

引用

页码：1059 / +

页数：2

共 50 条

[41] Fast and Accurate Classifier-Based Brain-Computer Interface System Using Single Channel EEG Data
Maleki, Masoud
Manshouri, Negin
Kayikcioglu, Temel
2018 26TH SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE (SIU), 2018,
[42] Discriminative Dictionary Learning for EEG Signal Classification in Brain-Computer Interface
Zhou, Wei
Yang, Ya
Yu, Zhuliang
2012 12TH INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION, ROBOTICS & VISION (ICARCV), 2012, : 1582 - 1585
[43] A comprehensive review of EEG-based brain-computer interface paradigms
Abiri, Reza
Borhani, Soheil
Sellers, Eric W.
Jiang, Yang
Zhao, Xiaopeng
JOURNAL OF NEURAL ENGINEERING, 2019, 16 (01)
[44] Sweet and Sour Taste Classification Using EEG Based Brain Computer Interface
Abidi, Ismi
Farooq, Omar
Beg, M. M. S.
2015 ANNUAL IEEE INDIA CONFERENCE (INDICON), 2015,
[45] EEG-Based Brain-Computer Interface for Control of Assistive Devices
Kapralov, Nikolay, V
Ekimovskii, Jaroslav, V
Potekhin, Vyacheslav V.
CYBER-PHYSICAL SYSTEMS AND CONTROL, 2020, 95 : 536 - 543
[46] An Optimized Quadratic Support Vector Machine for EEG Based Brain Computer Interface
Maher, Omar N.
Haikal, Amira Y.
Elhosseini, Mostafa A.
Saafan, Mahmoud
INTERNATIONAL JOURNAL OF ELECTRICAL AND COMPUTER ENGINEERING SYSTEMS, 2023, 14 (01) : 83 - 91
[47] EEG-Based Analysis of Auditory Stimulus in a Brain-Computer Interface
Naraballobh, Juti
Thanapatay, Dusit
Chinrungrueng, Jatuporn
Nishihara, Akinori
2015 6TH INTERNATIONAL CONFERENCE OF INFORMATION AND COMMUNICATION TECHNOLOGY FOR EMBEDDED SYSTEMS (IC-ICTES), 2015,
[48] Brain-Computer Interface with Corrupted EEG Data: a Tensor Completion Approach
Sole-Casals, J.
Caiafa, C. F.
Zhao, Q.
Cichocki, A.
COGNITIVE COMPUTATION, 2018, 10 (06) : 1062 - 1074
[49] Estimating the mutual information of an EEG-based brain-computer interface
Schlögl, A
Neuper, C
Pfurtscheller, G
BIOMEDIZINISCHE TECHNIK, 2002, 47 (1-2): : 3 - 8
[50] Partitioned Common Spatial Pattern Method for single trial EEG Signal classification in Brain-Computer Interface System
Sun, Hongyu
Bi, Lijun
Chen, Bisheng
AUTOMATIKA, 2016, 57 (01) : 66 - 75

← 1 2 3 4 5 →