BCI Competition 2003—Data Set Ia: Combining Gamma-Band Power With Slow Cortical Potentials to Improve Single-Trial Classification of Electroencephalographic Signals

2004 ◽  
Vol 51 (6) ◽  
pp. 1052-1056 ◽  
Author(s):  
B.D. Mensh ◽  
J. Werfel ◽  
H.S. Seung
Keyword(s):  
Gamma Band ◽  
Single Trial ◽  
Slow Cortical Potentials ◽  
Data Set ◽  
Band Power ◽  
Electroencephalographic Signals ◽  
Cortical Potentials

scholarly journals Real-time Detection of Driver’s Movement Intention in Response to Traffic Lights

2019 ◽  
Author(s):  
Zahra Khaliliardali ◽  
Ricardo Chavarriaga ◽  
Huaijian Zhang ◽  
Lucian A. Gheorghe ◽  
Serafeim Perdikis ◽  
...  
Keyword(s):  
Real Time ◽  
Area Under The Curve ◽  
Classification Performance ◽  
Brain State ◽  
Single Trial ◽  
Slow Cortical Potentials ◽  
Traffic Lights ◽  
Brain States ◽  
Movement Intention ◽  
Cortical Potentials

AbstractMovements are preceded by certain brain states that can be captured through various neuroimaging techniques. Brain-Computer Interfaces can be designed to detect the movement intention brain state during driving, which could be beneficial in improving the interaction between a smart car and its driver, by providing assistance in-line with the driver’s intention. In this paper, we present an Electroencephalogram based decoder of such brain states preceding movements performed in response to traffic lights in two experiments: in a car simulator and a real car. The results of both experiments (N=10: car simulator, N=8: real car) confirm the presence of anticipatory Slow Cortical Potentials in response to traffic lights for accelerating and braking actions. Single-trial classification performance exhibits an Area Under the Curve (AUC) of 0.71±0.14 for accelerating and 0.75±0.13 for braking. The AUC for the real car experiment are 0.63±0.07 and 0.64±0.13 for accelerating and braking respectively. Moreover, we evaluated the performance of real-time decoding of the intention to brake during online experiments only in the car simulator, yielding an average accuracy of 0.64±0.1. This paper confirm the existence of the anticipatory slow cortical potentials and the feasibility of single-trial detection these potentials in driving scenarios.

BAYESIAN CLASSIFICATION OF SINGLE-TRIAL EVENT-RELATED POTENTIALS IN EEG

2004 ◽  
Vol 14 (02) ◽  
pp. 719-726 ◽  
Author(s):  
JENS KOHLMORGEN ◽  
BENJAMIN BLANKERTZ
Keyword(s):  
Event Related Potentials ◽  
Computer Interface ◽  
Support Vector ◽  
Single Trial ◽  
Data Set ◽  
Classifier Design ◽  
Event Class ◽  
Related Potentials ◽  
Straightforward Approach

We present a systematic and straightforward approach to the problem of single-trial classification of event-related potentials (ERP) in EEG. Instead of using a generic classifier off-the-shelf, like a neural network or support vector machine, our classifier design is guided by prior knowledge about the problem and statistical properties found in the data. In particular, we exploit the well-known fact that event-related drifts in EEG potentials, albeit hard to detect in a single trial, can well be observed if averaged over a sufficiently large number of trials. We propose to use the average signal and its variance as a generative model for each event class and use Bayes' decision rule for the classification of new and unlabeled data. The method is successfully applied to a data set from the NIPS*2001 Brain–Computer Interface post-workshop competition. Our result turned out to be competitive with the best result of the competition.

Machine Learning Verdict of EEG Signals in Brain Computer Interface

2018 ◽  
pp. 429-441
Author(s):  
M. Jeyanthi ◽  
C. Velayutham
Keyword(s):  
Nearest Neighbor ◽  
Technology Development ◽  
Vital Role ◽  
Svm Classifier ◽  
K Nearest Neighbor ◽  
Data Mining Technique ◽  
Data Set ◽  
Eeg Data ◽  
Irrelevant Attributes

In Science and Technology Development BCI plays a vital role in the field of Research. Classification is a data mining technique used to predict group membership for data instances. Analyses of BCI data are challenging because feature extraction and classification of these data are more difficult as compared with those applied to raw data. In this paper, We extracted features using statistical Haralick features from the raw EEG data . Then the features are Normalized, Binning is used to improve the accuracy of the predictive models by reducing noise and eliminate some irrelevant attributes and then the classification is performed using different classification techniques such as Naïve Bayes, k-nearest neighbor classifier, SVM classifier using BCI dataset. Finally we propose the SVM classification algorithm for the BCI data set.

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