Khademul Islam Molla

Bio: Khademul Islam Molla is an academic researcher from University of Rajshahi. The author has contributed to research in topics: Support vector machine & Tangent space. The author has an hindex of 5, co-authored 7 publications receiving 72 citations.

Multiband tangent space mapping and feature selection for classification of EEG during motor imagery.

Abstract: OBJECTIVE When designing multiclass motor imagery-based brain-computer interface (MI-BCI), a so-called tangent space mapping (TSM) method utilizing the geometric structure of covariance matrices is an effective technique. This paper aims to introduce a method using TSM for finding accurate operational frequency bands related brain activities associated with MI tasks. APPROACH A multichannel electroencephalogram (EEG) signal is decomposed into multiple subbands, and tangent features are then estimated on each subband. A mutual information analysis-based effective algorithm is implemented to select subbands containing features capable of improving motor imagery classification accuracy. Thus obtained features of selected subbands are combined to get feature space. A principal component analysis-based approach is employed to reduce the features dimension and then the classification is accomplished by a support vector machine (SVM). MAIN RESULTS Offline analysis demonstrates the proposed multiband tangent space mapping with subband selection (MTSMS) approach outperforms state-of-the-art methods. It acheives the highest average classification accuracy for all datasets (BCI competition dataset 2a, IIIa, IIIb, and dataset JK-HH1). SIGNIFICANCE The increased classification accuracy of MI tasks with the proposed MTSMS approach can yield effective implementation of BCI. The mutual information-based subband selection method is implemented to tune operation frequency bands to represent actual motor imagery tasks.

Unsupervised frequency-recognition method of SSVEPs using a filter bank implementation of binary subband CCA.

Abstract: Objective Recently developed effective methods for detection commands of steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) that need calibration for visual stimuli, which cause more time and fatigue prior to the use, as the number of commands increases. This paper develops a novel unsupervised method based on canonical correlation analysis (CCA) for accurate detection of stimulus frequency. Approach A novel unsupervised technique termed as binary subband CCA (BsCCA) is implemented in a multiband approach to enhance the frequency recognition performance of SSVEP. In BsCCA, two subbands are used and a CCA-based correlation coefficient is computed for the individual subbands. In addition, a reduced set of artificial reference signals is used to calculate CCA for the second subband. The analyzing SSVEP is decomposed into multiple subband and the BsCCA is implemented for each one. Then, the overall recognition score is determined by a weighted sum of the canonical correlation coefficients obtained from each band. Main results A 12-class SSVEP dataset (frequency range: 9.25-14.75 Hz with an interval of 0.5 Hz) for ten healthy subjects are used to evaluate the performance of the proposed method. The results suggest that BsCCA significantly improves the performance of SSVEP-based BCI compared to the state-of-the-art methods. The proposed method is an unsupervised approach with averaged information transfer rate (ITR) of 77.04 bits min-1 across 10 subjects. The maximum individual ITR is 107.55 bits min-1 for 12-class SSVEP dataset, whereas, the ITR of 69.29 and 69.44 bits min-1 are achieved with CCA and NCCA respectively. Significance The statistical test shows that the proposed unsupervised method significantly improves the performance of the SSVEP-based BCI. It can be usable in real world applications.

Classification of motor imagery BCI using multiband tangent space mapping

Abstract: The tangent space mapping (TSM) becomes an effective method to implement brain computer interface (BCI) with motor imagery. In this paper, TSM is employed with multiband approach to extract discriminative features from electroencephalogram (EEG) to enhance classification accuracy. The EEG is decomposed into multiple subbands and the sample covariance matrices (SCMs) are then estimated on each of the subbands. Those matrices are then mapped onto the tangent space yielding the features. These obtained features of individual subbands are combined together. The dimension of the features space is reduced using principal component analysis (PCA) with one-way ANOVA. Support vector machine (SVM) based classification is performed employing the features with reduced dimension. The results of binary and four-class classification with public data sets showed that the proposed method significantly improved the performance compared to the state-of-the-art methods.

Phase synchronization analysis of EEG channels using bivariate empirical mode decomposition

Abstract: The paper presents a novel concept implementing a phase locking value index estimation in application to brain-computer interfacing (BCI) motor imagery paradigm. We propose to decompose first the pairs of EEG channels using a bivariate empirical mode decomposition (BEMD) method. Next, the phase locking values (PLV) are estimated for the obtained intrinsic mode functions resulting in discriminating features drawn from EEG channel pairs representing the two different lateral hemispheres. Numerical results suggest that the PLV induced from BEMD can effectively detect phase synchrony between electrodes and is a promising feature for BCI implementation.

Current Status, Challenges, and Possible Solutions of EEG-Based Brain-Computer Interface: A Comprehensive Review

Abstract: Brain-Computer Interface (BCI), in essence, aims at controlling different assistive devices through the utilization of brain waves. It is worth noting that the application of BCI is not limited to medical applications, and hence, the research in this field has gained due attention. Moreover, the significant number of related publications over the past two decades further indicates the consistent improvements and breakthroughs that have been made in this particular field. Nonetheless, it is also worth mentioning that with these improvements, new challenges are constantly discovered. This article provides a comprehensive review of the state-of-the-art of a complete BCI system. First, a brief overview of electroencephalogram (EEG)-based BCI systems is given. Secondly, a considerable number of popular BCI applications are reviewed in terms of electrophysiological control signals, feature extraction, classification algorithms, and performance evaluation metrics. Finally, the challenges to the recent BCI systems are discussed, and possible solutions to mitigate the issues are recommended.

Signal processing techniques for motor imagery brain computer interface: A review

Abstract: Motor Imagery Brain Computer Interface (MI-BCI) provides a non-muscular channel for communication to those who are suffering from neuronal disorders. The designing of an accurate and reliable MI-BCI system requires the extraction of informative and discriminative features. Common Spatial Pattern (CSP) has been potent and is widely used in BCI for extracting features in motor imagery tasks. The classifiers translate these features into device commands. Many classification algorithms have been devised, among those Support Vector Machine (SVM) and Linear Discriminate Analysis (LDA) have been widely used. In recent studies, the researchers are using deep neural networks for the classification of motor imagery tasks. This paper provides a comprehensive review of dominant feature extraction methods and classification algorithms in brain-computer interface for motor imagery tasks. Authors discuss existing challenges in the domain of motor imagery brain-computer interface and suggest possible research directions.

To train or not to train? A survey on training of feature extraction methods for SSVEP-based BCIs.

Abstract: Objective Despite the vast research aimed at improving the performance of steady-state visually evoked potential (SSVEP)-based brain-computer interfaces (BCIs), several limitations exist that restrict the use of such applications for long-term users in the real-world. One of the main challenges has been to reduce training time while maintaining good BCI performance. In view of this challenge, this survey identifies and compares the different training requirements of feature extraction methods for SSVEP-based BCIs. Approach This paper reviews the various state-of-the-art SSVEP feature extraction methods that have been developed and are most widely used in the literature. Main results The main contributions compared to existing reviews are the following: (i) a detailed summary, including a brief mathematical description of each feature extraction algorithm, providing a guide to the basic concepts of the state-of-the-art techniques for SSVEP-based BCIs found in literature; (ii) a categorisation of the training requirements of SSVEP-based methods into three categories, defined as training-free methods, subject-specific and subject-independent training methods; (iii) a comparative review of the training requirements of SSVEP feature extraction methods, providing a reference for future work on SSVEP-based BCIs. Significance This review highlights the strengths and weaknesses of the three categories of SSVEP training methods. Training-free systems are more practical but their performance is limited due to inter-subject variability resulting from the complex EEG activity. Feature extraction methods that incorporate some training data address this issue and in fact have outperformed training-free methods: subject-specific BCIs are tuned to the individual yielding the best performance at the cost of long, tiring training sessions making these methods unsuitable for everyday use; subject-independent BCIs that make use of training data from various subjects offer a good trade-off between training effort and performance, making these BCIs better suited for practical use.

Groundwater level forecasting with artificial neural networks: a comparison of long short-term memory (LSTM), convolutional neural networks (CNNs), and non-linear autoregressive networks with exogenous input (NARX)

Abstract: . It is now well established to use shallow artificial neural networks (ANNs) to obtain accurate and reliable groundwater level forecasts, which are an important tool for sustainable groundwater management. However, we observe an increasing shift from conventional shallow ANNs to state-of-the-art deep-learning (DL) techniques, but a direct comparison of the performance is often lacking. Although they have already clearly proven their suitability, shallow recurrent networks frequently seem to be excluded from the study design due to the euphoria about new DL techniques and its successes in various disciplines. Therefore, we aim to provide an overview on the predictive ability in terms of groundwater levels of shallow conventional recurrent ANNs, namely non-linear autoregressive networks with exogenous input (NARX) and popular state-of-the-art DL techniques such as long short-term memory (LSTM) and convolutional neural networks (CNNs). We compare the performance on both sequence-to-value (seq2val) and sequence-to-sequence (seq2seq) forecasting on a 4-year period while using only few, widely available and easy to measure meteorological input parameters, which makes our approach widely applicable. Further, we also investigate the data dependency in terms of time series length of the different ANN architectures. For seq2val forecasts, NARX models on average perform best; however, CNNs are much faster and only slightly worse in terms of accuracy. For seq2seq forecasts, mostly NARX outperform both DL models and even almost reach the speed of CNNs. However, NARX are the least robust against initialization effects, which nevertheless can be handled easily using ensemble forecasting. We showed that shallow neural networks, such as NARX, should not be neglected in comparison to DL techniques especially when only small amounts of training data are available, where they can clearly outperform LSTMs and CNNs; however, LSTMs and CNNs might perform substantially better with a larger dataset, where DL really can demonstrate its strengths, which is rarely available in the groundwater domain though.

Correlated Component Analysis for Enhancing the Performance of SSVEP-Based Brain-Computer Interface

Abstract: A new method for steady-state visual evoked potentials (SSVEPs) frequency recognition is proposed to enhance the performance of SSVEP-based brain-computer interface (BCI). Correlated component analysis (CORCA) is introduced, which originally was designed to find linear combinations of electrodes that are consistent across subjects and maximally correlated between them. We propose a CORCA algorithm to learn spatial filters with multiple blocks of individual training data for SSVEP-based BCI scenario. The spatial filters are used to remove background noises by combining the multichannel electroencephalogram signals. We conduct a comparison between the proposed CORCA-based and the task-related component analysis (TRCA) based methods using a 40-class SSVEP benchmark data set recorded from 35 subjects. Our experimental study validates the efficiency of the CORCA-based method, and the extensive comparison results indicate that the CORCA-based method significantly outperforms the TRCA-based method. Superior performance demonstrates that the proposed method holds the promising potential to achieve satisfactory performance for SSVEP-based BCI with a large number of targets.