EEG-based Single-trial Detection of Errors from Multiple Error-related Brain Activity

被引:0
作者
Shou, Guofa [1 ]
Ding, Lei [1 ,2 ]
机构
[1] Univ Oklahoma, Sch Elect & Comp Engn, Norman, OK 73019 USA
[2] Univ Oklahoma, Stephenson Sch Biomed Engn, Norman, OK 73019 USA
来源
2016 38TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC) | 2016年
基金
美国国家科学基金会;
关键词
NEURAL MARKERS; PERFORMANCE; POTENTIALS; ALPHA;
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
A key ability of the human brain is to monitor erroneous events and adjust behaviors accordingly. Electrophysiological and neuroimaging studies have demonstrated different brain activities related to errors. Meanwhile, the recognition of error-related brain activity as one aspect of performance monitoring has been reported for potential applications in clinical neuroscience and brain-machine interface, where single-trial analysis and classification would provide novel insights on dynamic brain responses to errors. However, procedures of selecting features, as well as procedures of single-trial classification, are not fully investigated for optimal performance. In the present study, we investigated the performance of different configurations of feature extractions in both temporal and frequency domains, for discriminating response errors in a color-word matching Stroop task. Motivated by our previous investigations, we evaluated both temporal and frequency features with component signals, which were obtained from EEG signals via an independent component analysis (ICA). Five component signals (independent components, ICs), originated from the frontal, motor, parietal, and occipital areas, were included in detecting error-related brain activity from single-trial EEG data. The results showed that better performance can be achieved by optimizing time window and frequency range of selected features, sampling scheme of feature-related data, and training of classifiers. However, a simple combination of features from multiple component signals can only slightly improve the detection performance of errors in single-trial data as compared to the frontal IC only. More importantly, it is indicated that four ICs other than the frontal one also carry similar discriminative information about errors in both temporal and frequency domains. The fact suggests flexible means in detecting errors from EEG beyond the frontal brain areas, which might be very valuable in practical applications such that the frontal area is not accessible.
引用
收藏
页码:2764 / 2767
页数:4
相关论文
共 13 条
  • [1] Performance of a Simulated Adaptive BCI Based on Experimental Classification of Movement-Related and Error Potentials
    Artusi, Xavier
    Niazi, Imran Khan
    Lucas, Marie-Francoise
    Farina, Dario
    [J]. IEEE JOURNAL ON EMERGING AND SELECTED TOPICS IN CIRCUITS AND SYSTEMS, 2011, 1 (04) : 480 - 488
  • [2] Errare machinale est: the use of error-related potentials in brain-machine interfaces
    Chavarriaga, Ricardo
    Sobolewski, Aleksander
    Millan, Jose del R.
    [J]. FRONTIERS IN NEUROSCIENCE, 2014, 8
  • [3] SMOTE: Synthetic minority over-sampling technique
    Chawla, Nitesh V.
    Bowyer, Kevin W.
    Hall, Lawrence O.
    Kegelmeyer, W. Philip
    [J]. 2002, American Association for Artificial Intelligence (16)
  • [4] Neural markers of errors as endophenotypes in neuropsychiatric disorders
    Manoach, Dara S.
    Agam, Yigal
    [J]. FRONTIERS IN HUMAN NEUROSCIENCE, 2013, 7
  • [5] Prestimulus Alpha and Mu Activity Predicts Failure to Inhibit Motor Responses
    Mazaheri, Ali
    Nieuwenhuis, Ingrid L. C.
    van Dijk, Hanneke
    Jensen, Ole
    [J]. HUMAN BRAIN MAPPING, 2009, 30 (06) : 1791 - 1800
  • [6] Response error correction - A demonstration of improved human-machine performance using real-time EEG monitoring
    Parra, LC
    Spence, CD
    Gerson, AD
    Sajda, P
    [J]. IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, 2003, 11 (02) : 173 - 177
  • [7] Online detection of error-related potentials boosts the performance of mental typewriters
    Schmidt, Nico M.
    Blankertz, Benjamin
    Treder, Matthias S.
    [J]. BMC NEUROSCIENCE, 2012, 13
  • [8] Shou GF, 2014, IEEE ENG MED BIO, P6222, DOI 10.1109/EMBC.2014.6945050
  • [9] Detection of EEG Spatial-Spectral-Temporal Signatures of Errors: A Comparative Study of ICA-Based and Channel-Based Methods
    Shou, Guofa
    Ding, Lei
    [J]. BRAIN TOPOGRAPHY, 2015, 28 (01) : 47 - 61
  • [10] NEUROPHYSIOLOGY OF PERFORMANCE MONITORING AND ADAPTIVE BEHAVIOR
    Ullsperger, Markus
    Danielmeier, Claudia
    Jocham, Gerhard
    [J]. PHYSIOLOGICAL REVIEWS, 2014, 94 (01) : 35 - 79