Review of the BCI Competition IV

Tangermann, Michael; Müller, Klaus Robert; Aertsen, Ad; Birbaumer, Niels; Braun, Christoph; Brunner, Clemens; Leeb, Robert; Mehring, Carsten; Miller, Kai J.; Müller-Putz, Gernot; Nolte, Guido; Pfurtscheller, Gert; Preißl, Hubert; Schalk, Gerwin; Schlögl, Alois; Vidaurre, Carmen; Waldert, Stephan; Blankertz, Benjamin

doi:10.3389/fnins.2012.00055

Cited by 802 publications

(563 citation statements)

References 131 publications

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“…This makes a direct comparison of studies and the algorithms used difficult and impedes the achieving of generalizable insights. The BCI community solved this issue by the publication of several high-quality data sets and the initiation of BCI competitions (e.g., [127]). For the aBCI community, several groups published comprehensive data sets that allow for the comparison of algorithms in different affect-elicitation contexts.…”

Section: Challenges On the Road Toward Abcimentioning

confidence: 99%

A survey of affective brain computer interfaces: principles, state-of-the-art, and challenges

Mühl

Allison

Nijholt

et al. 2014

Brain-Computer Interfaces

234

159

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Affective states, moods and emotions, are an integral part of human nature: they shape our thoughts, govern the behavior of the individual, and influence our interpersonal relationships. The last decades have seen a growing interest in the automatic detection of such states from voice, facial expression, and physiological signals, primarily with the goal of enhancing human-computer interaction with an affective component. With the advent of brain-computer interface research, the idea of affective brain-computer interfaces (aBCI), enabling affect detection from brain signals, arose. In this article, we set out to survey the field of neurophysiology-based affect detection. We outline possible applications of aBCI in a general taxonomy of brain-computer interface approaches and introduce the core concepts of affect and their neurophysiological fundamentals. We show that there is a growing body of literature that evidences the capabilities, but also the limitations and challenges of affect detection from neurophysiological activity

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Section: Challenges On the Road Toward Abcimentioning

confidence: 99%

A survey of affective brain computer interfaces: principles, state-of-the-art, and challenges

Mühl

Allison

Nijholt

et al. 2014

Brain-Computer Interfaces

234

159

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“…Only the class 1 and the class 2 for left hand and right hand were considered in this study (i.e., 144 trials). For more details about the dataset kindly refer to (Tangermann et al 2012). The motor imagery data from the session-I were used to train the classifiers, and the motor imagery data from the session-II were used as the test dataset.…”

Section: Bci Competition IV Dataset 2amentioning

confidence: 99%

“…The BCI competition IV dataset 2A (Tangermann et al 2012) is comprised of the EEG data collected from nine subjects, namely (A01-A09), that were recorded during two sessions on separate days for each subject. The data consist of 25 channels, which include 22 EEG channels, and 3 monopolar EOG channels.…”

Section: Bci Competition IV Dataset 2amentioning

confidence: 99%

Adaptive learning with covariate shift-detection for motor imagery-based brain–computer interface

Raza

Cecotti

et al. 2015

Soft Comput

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A common assumption in traditional supervised learning is the similar probability distribution of data between the training phase and the testing/operating phase. When transitioning from the training to testing phase, a shift in the probability distribution of input data is known as a covariate shift. Covariate shifts commonly arise in a wide range of real-world systems such as electroencephalogram-based brain-computer interfaces (BCIs). In such systems, there is a necessity for continuous monitoring of the process behavior, and tracking the state of the covariate shifts to decide about initiating adaptation in a timely manner. This paper presents a covariate shift-detection and -adaptation methodology, and its application to motor imagery-based BCIs. A covariate shift-detection test based on an exponential weighted moving average model is used to detect the covariate shift in the features extracted from motor imagery-based brain responses. Following the covariate shift-detection test, the methodology initiates an adaptation by updating the classifier during the testing/operating phase. The usefulness of the proposed method is evaluated using real-world BCI datasets (i.e. BCI competition IV dataset 2A and 2B). The results show a statistically significant improvement in the classification accuracy of the BCI system over traditional learning and semi-supervised learning methods.

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“…The cues were displayed for a period of 4s which interleaved with 2s of blank screen and 2s with a fixation cross shown in the center of the screen. As in each run have 50 trials of each of the chosen two classes, give the total of 200 trials for each subject [24]. Eleven channels are used in this research, named as FC5, FCz, FC6, C5, C3, Cz, C4, C6, CP5, CPz and CP6.…”

Section: Data Descriptionmentioning

confidence: 99%

The Performance Analysis of K-Nearest Neighbors (K-NN) Algorithm for Motor Imagery Classification Based on EEG Signal

et al. 2017

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Abstract. Most EEG-based motor imagery classification research focuses on the feature extraction phase of machine learning, neglecting the crucial part for accurate classification which is the classification. In contrast, this paper concentrates on the classifier development where it thoroughly studies the performance analysis of k-Nearest Neighbour (k-NN) classifier on EEG data. In the literature, the Euclidean distance metric is routinely applied for EEG data classification. However, no thorough study has been conducted to evaluate the effect of other distance metrics to the classification accuracy. Therefore, this paper studies the effectiveness of five distance metrics of k-NN: Manhattan, Euclidean, Minkowski, Chebychev and Hamming. The experiment shows that the distance computations that provides the highest classification accuracy is the Minkowski distance with 70.08%. Hence, this demonstrates the significant effect of distance metrics to the k-NN accuracy where the Minknowski distance gives higher accuracy compared to the Euclidean. Our result also shows that the accuracy of k-NN is comparable to Support Vector Machine (SVM) with lower complexity for EEG classification.

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Review of the BCI Competition IV

Cited by 802 publications

References 131 publications

A survey of affective brain computer interfaces: principles, state-of-the-art, and challenges

A survey of affective brain computer interfaces: principles, state-of-the-art, and challenges

Adaptive learning with covariate shift-detection for motor imagery-based brain–computer interface

The Performance Analysis of K-Nearest Neighbors (K-NN) Algorithm for Motor Imagery Classification Based on EEG Signal

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