Filter Bank Common Spatial Pattern Algorithm Research Articles

Clariant Catalysts & Lummus Technology expand CATOFIN footprint with India's first PDH plant

In recent years, the use of Brain Computer Interface (BCI) systems has become more popular due to rehabilitation applications, easy installation, cheapness, and so forth.Brain Computer Interface Systems can receive brain signals and input them into a computer. These signals can then be analysed. An important feature of BCI systems is that they have the ability to help people with disabilities or special needs, so that these people can use the system to control external devices or computer applications.In the presented paper, we have used BCI-SSVEP-Training dataset and OpenVibe open-source software. In OpenVibe, the CSP (Common Spatial Pattern) is used to detect the frequency of the stimulus that each subject looks at. We also considered the stimulus frequency harmonics and used the FBCSP (Filter Bank Common Spatial Pattern) and compared the results with each other.This study shows that using the FBCSP algorithm with Butterworth filter and High-cut and Low-cut equal to ± 1 Hz on the considered stimuli (12, 15 and 20) can improve the classification and detection accuracy in SSVEP data while the harmonic stimuli is considered. Therefore, for frequencies of 12, 15, 20 Hertz the accuracies are 92.91%, 98.95% and 94.50% with an ITR of 76.34bitmin .

Enhancing detection of steady-state visual evoked potentials using frequency and harmonics of that frequency in OpenVibe

In recent years, the use of Brain Computer Interface (BCI) systems has become more popular due to rehabilitation applications, easy installation, cheapness, and so forth.Brain Computer Interface Systems can receive brain signals and input them into a computer. These signals can then be analysed. An important feature of BCI systems is that they have the ability to help people with disabilities or special needs, so that these people can use the system to control external devices or computer applications.In the presented paper, we have used BCI-SSVEP-Training dataset and OpenVibe open-source software. In OpenVibe, the CSP (Common Spatial Pattern) is used to detect the frequency of the stimulus that each subject looks at. We also considered the stimulus frequency harmonics and used the FBCSP (Filter Bank Common Spatial Pattern) and compared the results with each other.This study shows that using the FBCSP algorithm with Butterworth filter and High-cut and Low-cut equal to ± 1 Hz on the considered stimuli (12, 15 and 20) can improve the classification and detection accuracy in SSVEP data while the harmonic stimuli is considered. Therefore, for frequencies of 12, 15, 20 Hertz the accuracies are 92.91%, 98.95% and 94.50% with an ITR of 76.34bitmin .

Filter Bank Common Spatial Pattern Algorithm on BCI Competition IV Datasets 2a and 2b

The Common Spatial Pattern (CSP) algorithm is an effective and popular method for classifying 2-class motor imagery electroencephalogram (EEG) data, but its effectiveness depends on the subject-specific frequency band. This paper presents the Filter Bank Common Spatial Pattern (FBCSP) algorithm to optimize the subject-specific frequency band for CSP on Datasets 2a and 2b of the Brain-Computer Interface (BCI) Competition IV. Dataset 2a comprised 4 classes of 22 channels EEG data from 9 subjects, and Dataset 2b comprised 2 classes of 3 bipolar channels EEG data from 9 subjects. Multi-class extensions to FBCSP are also presented to handle the 4-class EEG data in Dataset 2a, namely, Divide-and-Conquer (DC), Pair-Wise (PW), and One-Versus-Rest (OVR) approaches. Two feature selection algorithms are also presented to select discriminative CSP features on Dataset 2b, namely, the Mutual Information-based Best Individual Feature (MIBIF) algorithm, and the Mutual Information-based Rough Set Reduction (MIRSR) algorithm. The single-trial classification accuracies were presented using 10 × 10-fold cross-validations on the training data and session-to-session transfer on the evaluation data from both datasets. Disclosure of the test data labels after the BCI Competition IV showed that the FBCSP algorithm performed relatively the best among the other submitted algorithms and yielded a mean kappa value of 0.569 and 0.600 across all subjects in Datasets 2a and 2b respectively.

Subject and class specific frequency bands selection for multiclass motor imagery classification

AbstractEEG‐based discrimination among motor imagery states has been widely studied for brain‐computer interfaces (BCIs) due to the great potential for real‐life applications. However, in terms of designing a motor imagery‐based BCI system, a lot of research in the literature either uses a frequency band of interest selected manually based on the visual analysis of EEG data or is set to a general broad band, causing performance degradation in classification. In this article, we propose a novel method of selecting subject and class specific frequency bands based on the analysis of a channel‐frequency matrix, which we call a channel‐frequency map. We operate the classification process for each frequency band individually, i.e., spatial filtering, feature extraction, and classification, and determine a class label for an input EEG by considering the outputs from multiple classifiers together at the end. From our experiments on a public dataset of BCI Competition IV (2008) II‐a that includes four motor imagery tasks from nine subjects, the proposed algorithm outperformed the common spatial pattern (CSP) algorithm in a broad band and a filter bank CSP algorithm on average in terms of cross‐validation and session‐to‐session transfer rate. Furthermore, a considerable increase of classification accuracy has been achieved for certain subjects. We also would like to note that the proposed data‐driven frequency bands selection method is applicable to other kinds of single‐trial EEG classifications that are based on modulations of brain rhythms, by no means limited to motor imagery‐based BCI applications. © 2011 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 21, 123–130, 2011