Machine-learning based co-adaptive calibration: A perspective to fight BCI illiteracy

Carmen Vidaurre; Claudia Sannelli; Klaus Robert Müller; Benjamin Blankertz

doi:10.1007/978-3-642-13769-3_50

Machine-learning based co-adaptive calibration: A perspective to fight BCI illiteracy

Carmen Vidaurre, Claudia Sannelli, Klaus Robert Müller, Benjamin Blankertz

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

12 Citations (Scopus)

Abstract

"BCI illiteracy" is one of the biggest problems and challenges in BCI research. It means that BCI control cannot be achieved by a non-negligible number of subjects (estimated 20% to 25%). There are two main causes for BCI illiteracy in BCI users: either no SMR idle rhythm is observed over motor areas, or this idle rhythm is not attenuated during motor imagery, resulting in a classification performance lower than 70% (criterion level) already for offline calibration data. In a previous work of the same authors, the concept of machine learning based co-adaptive calibration was introduced. This new type of calibration provided substantially improved performance for a variety of users. Here, we use a similar approach and investigate to what extent co-adapting learning enables substantial BCI control for completely novice users and those who suffered from BCI illiteracy before.

Original language	English
Title of host publication	Hybrid Artificial Intelligence Systems - 5th International Conference, HAIS 2010, Proceedings
Pages	413-420
Number of pages	8
Edition	PART 1
DOIs	https://doi.org/10.1007/978-3-642-13769-3_50
Publication status	Published - 2010
Externally published	Yes
Event	5th International Conference on Hybrid Artificial Intelligence Systems, HAIS 2010 - San Sebastian, Spain Duration: 2010 Jun 23 → 2010 Jun 25

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 1
Volume	6076 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	5th International Conference on Hybrid Artificial Intelligence Systems, HAIS 2010
Country/Territory	Spain
City	San Sebastian
Period	10/6/23 → 10/6/25

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/978-3-642-13769-3_50

Cite this

Vidaurre, C., Sannelli, C., Müller, K. R., & Blankertz, B. (2010). Machine-learning based co-adaptive calibration: A perspective to fight BCI illiteracy. In Hybrid Artificial Intelligence Systems - 5th International Conference, HAIS 2010, Proceedings (PART 1 ed., pp. 413-420). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6076 LNAI, No. PART 1). https://doi.org/10.1007/978-3-642-13769-3_50

Machine-learning based co-adaptive calibration: A perspective to fight BCI illiteracy. / Vidaurre, Carmen; Sannelli, Claudia; Müller, Klaus Robert et al.
Hybrid Artificial Intelligence Systems - 5th International Conference, HAIS 2010, Proceedings. PART 1. ed. 2010. p. 413-420 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6076 LNAI, No. PART 1).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Vidaurre, C, Sannelli, C, Müller, KR & Blankertz, B 2010, Machine-learning based co-adaptive calibration: A perspective to fight BCI illiteracy. in Hybrid Artificial Intelligence Systems - 5th International Conference, HAIS 2010, Proceedings. PART 1 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 1, vol. 6076 LNAI, pp. 413-420, 5th International Conference on Hybrid Artificial Intelligence Systems, HAIS 2010, San Sebastian, Spain, 10/6/23. https://doi.org/10.1007/978-3-642-13769-3_50

Vidaurre C, Sannelli C, Müller KR, Blankertz B. Machine-learning based co-adaptive calibration: A perspective to fight BCI illiteracy. In Hybrid Artificial Intelligence Systems - 5th International Conference, HAIS 2010, Proceedings. PART 1 ed. 2010. p. 413-420. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1). doi: 10.1007/978-3-642-13769-3_50

Vidaurre, Carmen ; Sannelli, Claudia ; Müller, Klaus Robert et al. / Machine-learning based co-adaptive calibration : A perspective to fight BCI illiteracy. Hybrid Artificial Intelligence Systems - 5th International Conference, HAIS 2010, Proceedings. PART 1. ed. 2010. pp. 413-420 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1).

@inproceedings{2c3b5260879f4c848d80bcade05e2063,

title = "Machine-learning based co-adaptive calibration: A perspective to fight BCI illiteracy",

abstract = "{"}BCI illiteracy{"} is one of the biggest problems and challenges in BCI research. It means that BCI control cannot be achieved by a non-negligible number of subjects (estimated 20% to 25%). There are two main causes for BCI illiteracy in BCI users: either no SMR idle rhythm is observed over motor areas, or this idle rhythm is not attenuated during motor imagery, resulting in a classification performance lower than 70% (criterion level) already for offline calibration data. In a previous work of the same authors, the concept of machine learning based co-adaptive calibration was introduced. This new type of calibration provided substantially improved performance for a variety of users. Here, we use a similar approach and investigate to what extent co-adapting learning enables substantial BCI control for completely novice users and those who suffered from BCI illiteracy before.",

author = "Carmen Vidaurre and Claudia Sannelli and M{\"u}ller, {Klaus Robert} and Benjamin Blankertz",

year = "2010",

doi = "10.1007/978-3-642-13769-3_50",

language = "English",

isbn = "3642137687",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

number = "PART 1",

pages = "413--420",

booktitle = "Hybrid Artificial Intelligence Systems - 5th International Conference, HAIS 2010, Proceedings",

edition = "PART 1",

note = "5th International Conference on Hybrid Artificial Intelligence Systems, HAIS 2010 ; Conference date: 23-06-2010 Through 25-06-2010",

}

TY - GEN

T1 - Machine-learning based co-adaptive calibration

T2 - 5th International Conference on Hybrid Artificial Intelligence Systems, HAIS 2010

AU - Vidaurre, Carmen

AU - Sannelli, Claudia

AU - Müller, Klaus Robert

AU - Blankertz, Benjamin

PY - 2010

Y1 - 2010

N2 - "BCI illiteracy" is one of the biggest problems and challenges in BCI research. It means that BCI control cannot be achieved by a non-negligible number of subjects (estimated 20% to 25%). There are two main causes for BCI illiteracy in BCI users: either no SMR idle rhythm is observed over motor areas, or this idle rhythm is not attenuated during motor imagery, resulting in a classification performance lower than 70% (criterion level) already for offline calibration data. In a previous work of the same authors, the concept of machine learning based co-adaptive calibration was introduced. This new type of calibration provided substantially improved performance for a variety of users. Here, we use a similar approach and investigate to what extent co-adapting learning enables substantial BCI control for completely novice users and those who suffered from BCI illiteracy before.

AB - "BCI illiteracy" is one of the biggest problems and challenges in BCI research. It means that BCI control cannot be achieved by a non-negligible number of subjects (estimated 20% to 25%). There are two main causes for BCI illiteracy in BCI users: either no SMR idle rhythm is observed over motor areas, or this idle rhythm is not attenuated during motor imagery, resulting in a classification performance lower than 70% (criterion level) already for offline calibration data. In a previous work of the same authors, the concept of machine learning based co-adaptive calibration was introduced. This new type of calibration provided substantially improved performance for a variety of users. Here, we use a similar approach and investigate to what extent co-adapting learning enables substantial BCI control for completely novice users and those who suffered from BCI illiteracy before.

UR - http://www.scopus.com/inward/record.url?scp=77954572685&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=77954572685&partnerID=8YFLogxK

U2 - 10.1007/978-3-642-13769-3_50

DO - 10.1007/978-3-642-13769-3_50

M3 - Conference contribution

AN - SCOPUS:77954572685

SN - 3642137687

SN - 9783642137686

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 413

EP - 420

BT - Hybrid Artificial Intelligence Systems - 5th International Conference, HAIS 2010, Proceedings

Y2 - 23 June 2010 through 25 June 2010

ER -

Machine-learning based co-adaptive calibration: A perspective to fight BCI illiteracy

Abstract

Publication series

Other

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this