Inter-session evaluation of the classification performance versus the number of EEG electrodes for different sensor selection approaches.

Comparison of Channel Selection Methods on the Classification of EEG Data Obtained from the Animal Non-animal Categorization Experiment

Ozbeyaz, Abdurrahman
Arica, Sami

January 2015

23nd Signal Processing and Communications Applications Conference (SIU) -- MAY 16-19, 2015 -- Inonu ...

Designing a multi trial classifier for EEG signals: classifying for EEG signals: classifying rhythms perceived

Kruif, B.J. de
Desain, P.W.M.

January 2006

Contains fulltext : 74669.pdf (publisher's version ) (Closed access

Comparison of decoding accuracies using different numbers of EEG channels.

Ke Liao (148279)
Ran Xiao (506702)
Jania Gonzalez (506703)
Lei Ding (186021)

January 2014

Data were from the optimal principal component set and averaged over all subjects. (a) Layout of ...

Intra-session evaluation of the classification performance versus the number of EEG electrodes for different sensor selection approaches.

David Feess (429126)
Mario M. Krell (429127)
Jan H. Metzen (429128)

July 2013

The horizontal line All is a reference showing the performance using all available 62 elec...

Comparison of Sensor Selection Mechanisms for an ERP- Based Brain-Computer Interface

David Feess
Mario M. Krell
Jan H. Metzen

January 2013

A major barrier for a broad applicability of brain-computer interfaces (BCIs) based on electroenceph...

Selecting methods for a modular EEG pre-processing pipeline: An objective comparison

Coelli S.
Calcagno A.
Cassani C. M.
Temporiti F.
Reali P.
Gatti R.
Galli M.
Bianchi A. M.

January 2024

Electroencephalography (EEG) to study brain functions has become fundamental in many research settin...

Comparative analysis of two-group supervised classification algorithms in the study of P300-based brain-computer interface

Plechawska-Wójcik Małgorzata
Kaczorowska Monika
Michalik Bernadetta

January 2019

The main goal of the paper is to perform a comparative accuracy analysis of the two-group classifica...

The 2007 AASM recommendations for EEG electrode placement in polysomnography: impact on sleep and cortical arousal scoring

Ruehland, W.
O'Donoghue, F.
Pierce, R.
Thornton, A.
Singh, P.
Copland, J.
Stevens, B.
Rochford, P.

January 2011

STUDY OBJECTIVE: To examine the impact of using American Academy of Sleep Medicine (AASM) recommende...

Toward Automated Electrode Selection in the Electronic Depth Control Strategy for Multi-unit Recordings

Van Dijck, Gert
Jezzini, Ahmad
Herwik, Stanislav
Kisban, Sebastian
Seidl, Karsten
Paul, Olivier
Ruther, Patrick
Serventi, Francesca Ugolotti
Fogassi, Leonardo
Van Hulle, Marc
Umiltà, Maria Alessandra

January 2010

Multi-electrode arrays contain an increasing number of electrodes. The manual selection of good qual...

A comparison between EEG power in the different frequency bands grouped by task performance (personal optimal solutions versus other solutions).

Hsiao-Ching She (313807)
Tzyy-Ping Jung (33)
Wen-Chi Chou (36444)
Li-Yu Huang (313813)
Chia-Yu Wang (315748)
Guan-Yu Lin (315749)

February 2013

A comparison between EEG power in the different frequency bands grouped by task performance (pers...

A framework for the comparison of different EEG acquisition solutions

Bussalb, Aurore
Prat, Marie
Ojeda, David
Barthélemy, Quentin
Bonnaud, Julien
Mayaud, Louis

October 2018

The purpose of this work is to propose a framework for the benchmarking of EEG amplifiers, headsets,...

Combining and comparing EEG, peripheral physiology and eye-related measures for the assessment of mental workload

Hogervorst, M.A.
Brouwer, A.M.
Erp, J.B.F. van

January 2014

While studies exist that compare different physiological variables with respect to their association...

Comparison of channel selection methods on the classification of EEG data obtained from the animal non-animal categorization experiment [Canli-Cansiz Nesne Deneyine Ait EEG Işaretlerinin Siniflandirilmasinda Kanal Seçim Yöntemlerinin Karşilaştirilmasi]

Ozbeyaz A.
Arica S.

January 2015

2015 23rd Signal Processing and Communications Applications Conference, SIU 2015 --16 May 2015 throu...

The impact of electrode characteristics on electrocorticography (ECoG)

Wodlinger, B
Degenhart, AD
Collinger, JL
Tyler-Kabara, EC
Wang, W

December 2011

Used clinically since Penfield and Jasper's pioneering work in the 1950's, electrocorticography (ECo...

Systems, Subjects, Sessions: to what extent do these factors influence EEG data?

Melnik, Andrew
Legkov, Petr
Izdebski, Krzysztof
Kärcher, Silke Manuela
Hairston, W. David
Ferris, Daniel P.
König, Peter

May 2017

Lab-based electroencephalography (EEG) techniques have matured over decades of research and can prod...

Comparison of Channel Selection Methods on the Classification of EEG Data Obtained from the Animal Non-animal Categorization Experiment

Ozbeyaz, Abdurrahman
Arica, Sami

January 2015

23nd Signal Processing and Communications Applications Conference (SIU) -- MAY 16-19, 2015 -- Inonu ...

Designing a multi trial classifier for EEG signals: classifying for EEG signals: classifying rhythms perceived

Kruif, B.J. de
Desain, P.W.M.

January 2006

Contains fulltext : 74669.pdf (publisher's version ) (Closed access

Comparison of decoding accuracies using different numbers of EEG channels.

Ke Liao (148279)
Ran Xiao (506702)
Jania Gonzalez (506703)
Lei Ding (186021)

January 2014

Data were from the optimal principal component set and averaged over all subjects. (a) Layout of ...

Intra-session evaluation of the classification performance versus the number of EEG electrodes for different sensor selection approaches.

David Feess (429126)
Mario M. Krell (429127)
Jan H. Metzen (429128)

July 2013

The horizontal line All is a reference showing the performance using all available 62 elec...

Comparison of Sensor Selection Mechanisms for an ERP- Based Brain-Computer Interface

David Feess
Mario M. Krell
Jan H. Metzen

January 2013

A major barrier for a broad applicability of brain-computer interfaces (BCIs) based on electroenceph...

Selecting methods for a modular EEG pre-processing pipeline: An objective comparison

Coelli S.
Calcagno A.
Cassani C. M.
Temporiti F.
Reali P.
Gatti R.
Galli M.
Bianchi A. M.

January 2024

Electroencephalography (EEG) to study brain functions has become fundamental in many research settin...

Comparative analysis of two-group supervised classification algorithms in the study of P300-based brain-computer interface

Plechawska-Wójcik Małgorzata
Kaczorowska Monika
Michalik Bernadetta

January 2019

The main goal of the paper is to perform a comparative accuracy analysis of the two-group classifica...

The 2007 AASM recommendations for EEG electrode placement in polysomnography: impact on sleep and cortical arousal scoring

Ruehland, W.
O'Donoghue, F.
Pierce, R.
Thornton, A.
Singh, P.
Copland, J.
Stevens, B.
Rochford, P.

January 2011

STUDY OBJECTIVE: To examine the impact of using American Academy of Sleep Medicine (AASM) recommende...

Toward Automated Electrode Selection in the Electronic Depth Control Strategy for Multi-unit Recordings

Van Dijck, Gert
Jezzini, Ahmad
Herwik, Stanislav
Kisban, Sebastian
Seidl, Karsten
Paul, Olivier
Ruther, Patrick
Serventi, Francesca Ugolotti
Fogassi, Leonardo
Van Hulle, Marc
Umiltà, Maria Alessandra

January 2010

Multi-electrode arrays contain an increasing number of electrodes. The manual selection of good qual...

A comparison between EEG power in the different frequency bands grouped by task performance (personal optimal solutions versus other solutions).

Hsiao-Ching She (313807)
Tzyy-Ping Jung (33)
Wen-Chi Chou (36444)
Li-Yu Huang (313813)
Chia-Yu Wang (315748)
Guan-Yu Lin (315749)

February 2013

A comparison between EEG power in the different frequency bands grouped by task performance (pers...

A framework for the comparison of different EEG acquisition solutions

Bussalb, Aurore
Prat, Marie
Ojeda, David
Barthélemy, Quentin
Bonnaud, Julien
Mayaud, Louis

October 2018

The purpose of this work is to propose a framework for the benchmarking of EEG amplifiers, headsets,...

Combining and comparing EEG, peripheral physiology and eye-related measures for the assessment of mental workload

Hogervorst, M.A.
Brouwer, A.M.
Erp, J.B.F. van

January 2014

While studies exist that compare different physiological variables with respect to their association...

Comparison of channel selection methods on the classification of EEG data obtained from the animal non-animal categorization experiment [Canli-Cansiz Nesne Deneyine Ait EEG Işaretlerinin Siniflandirilmasinda Kanal Seçim Yöntemlerinin Karşilaştirilmasi]

Ozbeyaz A.
Arica S.

January 2015

2015 23rd Signal Processing and Communications Applications Conference, SIU 2015 --16 May 2015 throu...

The impact of electrode characteristics on electrocorticography (ECoG)

Wodlinger, B
Degenhart, AD
Collinger, JL
Tyler-Kabara, EC
Wang, W

December 2011

Used clinically since Penfield and Jasper's pioneering work in the 1950's, electrocorticography (ECo...

Systems, Subjects, Sessions: to what extent do these factors influence EEG data?

Melnik, Andrew
Legkov, Petr
Izdebski, Krzysztof
Kärcher, Silke Manuela
Hairston, W. David
Ferris, Daniel P.
König, Peter

May 2017

Lab-based electroencephalography (EEG) techniques have matured over decades of research and can prod...

Comparison of Channel Selection Methods on the Classification of EEG Data Obtained from the Animal Non-animal Categorization Experiment

Ozbeyaz, Abdurrahman
Arica, Sami

January 2015

23nd Signal Processing and Communications Applications Conference (SIU) -- MAY 16-19, 2015 -- Inonu ...

Designing a multi trial classifier for EEG signals: classifying for EEG signals: classifying rhythms perceived

Kruif, B.J. de
Desain, P.W.M.

January 2006

Contains fulltext : 74669.pdf (publisher's version ) (Closed access

Comparison of decoding accuracies using different numbers of EEG channels.

Ke Liao (148279)
Ran Xiao (506702)
Jania Gonzalez (506703)
Lei Ding (186021)

January 2014

Data were from the optimal principal component set and averaged over all subjects. (a) Layout of ...

Inter-session evaluation of the classification performance versus the number of EEG electrodes for different sensor selection approaches.

Abstract

Extracted data

Inter-session evaluation of the classification performance versus the number of EEG electrodes for different sensor selection approaches.

Abstract

Extracted data

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