Single-trial analysis and classification of ERP components — A tutorial

Blankertz, Benjamin; Lemm, Steven; Treder, Matthias S.; Haufe, Stefan; Müller, Klaus‐Robert

doi:10.1016/j.neuroimage.2010.06.048

Cited by 1,007 publications

(905 citation statements)

References 60 publications

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“…Movements with an angular deviance of 0°were labeled as class 1, and movements with an absolute deviance of 135°or more were labeled as class 2. A regularized linear discriminant analysis classifier was trained to separate classes (13). The open-source toolbox BCILAB (28) version 1.01 was used to define and implement the pBCI.…”

Section: Methodsmentioning

confidence: 99%

Neuroadaptive technology enables implicit cursor control based on medial prefrontal cortex activity

Zander

Krol

Birbaumer

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

135

124

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The effectiveness of today's human-machine interaction is limited by a communication bottleneck as operators are required to translate high-level concepts into a machine-mandated sequence of instructions. In contrast, we demonstrate effective, goal-oriented control of a computer system without any form of explicit communication from the human operator. Instead, the system generated the necessary input itself, based on real-time analysis of brain activity. Specific brain responses were evoked by violating the operators' expectations to varying degrees. The evoked brain activity demonstrated detectable differences reflecting congruency with or deviations from the operators' expectations. Real-time analysis of this activity was used to build a user model of those expectations, thus representing the optimal (expected) state as perceived by the operator. Based on this model, which was continuously updated, the computer automatically adapted itself to the expectations of its operator. Further analyses showed this evoked activity to originate from the medial prefrontal cortex and to exhibit a linear correspondence to the degree of expectation violation. These findings extend our understanding of human predictive coding and provide evidence that the information used to generate the user model is task-specific and reflects goal congruency. This paper demonstrates a form of interaction without any explicit input by the operator, enabling computer systems to become neuroadaptive, that is, to automatically adapt to specific aspects of their operator's mindset. Neuroadaptive technology significantly widens the communication bottleneck and has the potential to fundamentally change the way we interact with technology.human-computer interaction | passive brain-computer interfaces | electroencephalogram | predictive coding | neuroadaptive technology I n the European Union, 96% of enterprises rely on computers for their productivity (1). Advances in human-computer interaction (HCI), concerning the effective, efficient, and satisfying use of computer systems, may thus carry great societal benefits, e.g., in terms of productivity. However, although interaction techniques have become increasingly user-friendly-e.g., from punch cards to touch screens-they still depend on the user (operator) to translate their original thought or intention into a sequence of small, explicit commands (2). This translational step, where the human operator must ultimately obey the machine's logic, presents both a communication bottleneck and a source of potential error (3). At the same time, the computer has practically no limitation to the amount of information it can communicate, and is not as adaptable as its user. In these aspects, present-day HCI is asymmetrical (4). Comparing this to human-human interaction, Fischer (5) emphasizes the importance of a shared understanding of the situation and an understanding of the communication partner. In this sense, for a computer system to "understand" its user, it needs a model of that user-a source of informa...

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Section: Methodsmentioning

confidence: 99%

Neuroadaptive technology enables implicit cursor control based on medial prefrontal cortex activity

Zander

Krol

Birbaumer

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

135

124

View full text Add to dashboard Cite

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“…In order to prevent errors due to bad estimation of covariance matrices (bias), a shrinkage regularization was implemented [27].…”

Section: Classification Stagementioning

confidence: 99%

Optimization of EMG-based hand gesture recognition: Supervised vs. unsupervised data preprocessing on healthy subjects and transradial amputees

Riillo

Quitadamo

Cavrini

et al. 2014

Biomedical Signal Processing and Control

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a b s t r a c tWe propose a methodological study for the optimization of surface EMG (sEMG)-based hand gesture classification, effective to implement a human-computer interaction device for both healthy subjects and transradial amputees. The widely commonly used unsupervised Principal Component Analysis (PCA) approach was compared to the promising supervised common spatial pattern (CSP) methodology to identify the best classification strategy and the related tuning parameters. A low density array of sEMG sensors was built to record the muscular activity of the forearm and classify five different hand gestures. Twenty healthy subjects were recruited to compute optimized parameters for ("within" analysis) and to compare between ("between" analysis) the two strategies. The system was also tested on a transradial amputee subject, in order to assess the robustness of the optimization in recognizing disabled users' gestures.Results show that RMS-WA/ANN is the best feature vector/classifier pair for the PCA approach (accuracy 88.81 ± 6.58%), whereas M-RMS-WA/ANN is the best pair for the CSP methodology (accuracy of 89.35 ± 6.16%). Statistical analysis on classification results shows no significant differences between the two strategies. Moreover we found out that the optimization computed for healthy subjects was proven to be sufficiently robust to be used on the amputee subject. This motivates further investigation of the proposed methodology on a larger sample of amputees. Our results are useful to boost EMG-based hand gesture recognition and constitute a step toward the definition of an efficient EMG-controlled system for amputees.

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“…However, it was difficult to train an excellent subject who could proficiently control his/her brain signals. To relieve the inevitable trial-to-trial variation in ERP components due to fatigue and changes in task involvement during the experiments, adaptive algorithms, statistical signal processing and machine learning methods help to improve the classification accuracy of a single-trial EEG (Blankertz et al 2011). The adaptive classifier proposed by Shenoy corrects the bias between calibration and feedback sessions in combination with an offline feature selection (Shenoy et al 2006).…”

Section: A Brain-computer Interface (Bci) Is a Communication Ormentioning

confidence: 99%

“…The LDA for binary problems follows the optimal projection vector w, corresponding to the largest J values (Blankertz et al 2011;Duda et al 2001).…”

Section: Linear Classifiermentioning

confidence: 99%

Improving N1 classification by grouping EEG trials with phases of pre-stimulus EEG oscillations

Han

Zhang

et al. 2014

Cogn Neurodyn

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A reactive brain-computer interface using electroencephalography (EEG) relies on the classification of evoked ERP responses. As the trial-to-trial variation is evitable in EEG signals, it is a challenge to capture the consistent classification features distribution. Clustering EEG trials with similar features and utilizing a specific classifier adjusted to each cluster can improve EEG classification. In this paper, instead of measuring the similarity of ERP features, the brain states during image stimuli presentation that evoked N1 responses were used to group EEG trials. The correlation between momentary phases of pre-stimulus EEG oscillations and N1 amplitudes was analyzed. The results demonstrated that the phases of timefrequency points about 5.3 Hz and 0.3 s before the stimulus onset have significant effect on the ERP classification accuracy. Our findings revealed that N1 components in ERP fluctuated with momentary phases of EEG. We also further studied the influence of pre-stimulus momentary phases on classification of N1 features. Results showed that linear classifiers demonstrated outstanding classification performance when training and testing trials have close momentary phases. Therefore, this gave us a new direction to improve EEG classification by grouping EEG trials with similar pre-stimulus phases and using each to train unit classifiers respectively.

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Single-trial analysis and classification of ERP components — A tutorial

Cited by 1,007 publications

References 60 publications

Neuroadaptive technology enables implicit cursor control based on medial prefrontal cortex activity

Neuroadaptive technology enables implicit cursor control based on medial prefrontal cortex activity

Optimization of EMG-based hand gesture recognition: Supervised vs. unsupervised data preprocessing on healthy subjects and transradial amputees

Improving N1 classification by grouping EEG trials with phases of pre-stimulus EEG oscillations

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