Multiclass EEG signal classification utilizing Rényi min-entropy-based feature selection from wavelet packet transformation

Rahman, Md. Asadur; Khanam, Farzana; Ahmad, Mohiuddin; Uddin, Mohammad Shorif

doi:10.1186/s40708-020-00108-y

Cited by 36 publications

(16 citation statements)

References 38 publications

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“…Table 7 has listed the summary of some recent studies where data-4 has been classified by means of different methods. Authors in Rahman et al (2020) proposed wavelet packet transform (WPT)-MLP based approach to classify data-4. Besides this, the Rényi min-entropy has been implemented to identify the significant features, and an accuracy of 80.11 ± 4.28% was achieved.…”

Section: Discussionmentioning

confidence: 99%

The classification of motor imagery response: an accuracy enhancement through the ensemble of random subspace k-NN

Rashid

Bari

Hasan

et al. 2021

PeerJ Computer Science

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Brain-computer interface (BCI) is a viable alternative communication strategy for patients of neurological disorders as it facilitates the translation of human intent into device commands. The performance of BCIs primarily depends on the efficacy of the feature extraction and feature selection techniques, as well as the classification algorithms employed. More often than not, high dimensional feature set contains redundant features that may degrade a given classifier’s performance. In the present investigation, an ensemble learning-based classification algorithm, namely random subspace k-nearest neighbour (k-NN) has been proposed to classify the motor imagery (MI) data. The common spatial pattern (CSP) has been applied to extract the features from the MI response, and the effectiveness of random forest (RF)-based feature selection algorithm has also been investigated. In order to evaluate the efficacy of the proposed method, an experimental study has been implemented using four publicly available MI dataset (BCI Competition III dataset 1 (data-1), dataset IIIA (data-2), dataset IVA (data-3) and BCI Competition IV dataset II (data-4)). It was shown that the ensemble-based random subspace k-NN approach achieved the superior classification accuracy (CA) of 99.21%, 93.19%, 93.57% and 90.32% for data-1, data-2, data-3 and data-4, respectively against other models evaluated, namely linear discriminant analysis, support vector machine, random forest, Naïve Bayes and the conventional k-NN. In comparison with other classification approaches reported in the recent studies, the proposed method enhanced the accuracy by 2.09% for data-1, 1.29% for data-2, 4.95% for data-3 and 5.71% for data-4, respectively. Moreover, it is worth highlighting that the RF feature selection technique employed in the present study was able to significantly reduce the feature dimension without compromising the overall CA. The outcome from the present study implies that the proposed method may significantly enhance the accuracy of MI data classification.

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Section: Discussionmentioning

confidence: 99%

The classification of motor imagery response: an accuracy enhancement through the ensemble of random subspace k-NN

Rashid

Bari

Hasan

et al. 2021

PeerJ Computer Science

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“…Third, issues concerning feature extraction and selection were increasingly of concern to scholars (e.g., [247][248][249][250][251][252][253]). Recently, combining EEG signal feature extraction and classification methods have been widely used to identify mild cognitive impairments [254], driving fatigue state [250], and familiar and unfamiliar persons [252].…”

Section: Latest Research Concerning Ai-enhanced Eeg Analysismentioning

confidence: 99%

“…In addition, feature selection is an important yet challenging issue, since there are numerous features, a small amount of clinical data exists, and there are many similarities between selected features. Recent studies have applied techniques such as Rényi minentropy-based feature selection [249], common spatial pattern-based feature selection [251], and principal component analysis-based feature selection [253].…”

Section: Latest Research Concerning Ai-enhanced Eeg Analysismentioning

confidence: 99%

Global research on artificial intelligence-enhanced human electroencephalogram analysis

Chen

Tao

Wang

et al. 2021

Neural Comput & Applic

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The application of artificial intelligence (AI) technologies in assisting human electroencephalogram (EEG) analysis has become an active scientific field. This study aims to present a comprehensive review of the research field of AI-enhanced human EEG analysis. Using bibliometrics and topic modeling, research articles concerning AI-enhanced human EEG analysis collected from the Web of Science database during the period 2009-2018 were analyzed. After examining 2053 research articles published around the world, it was found that the annual number of articles had significantly grown from 78 to 468, with the USA and China being the most influential and prolific. The results of the keyword analysis showed that ''electroencephalogram,'' ''brain-computer interface,'' ''classification,'' ''support vector machine,'' ''electroencephalography,'' and ''signal'' were the most frequently used. The results of topic modeling and evolution analyses highlighted several important issues, including epileptic seizure detection, brain-machine interface, EEG classification, mental disorders, emotion, and alcoholism and anesthesia. The findings suggest that such visualization and analysis of the research articles could provide a comprehensive overview of the field for communities of practice and inquiry worldwide.

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“…For details on classification and learning problems and their algorithms see [ 1 ]. Moreover, we can find in the machine learning literature many papers in which different concepts and methods from information entropy are used together with learning classification algorithms to design new classifiers to be applied in different contexts [ 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 ].…”

Section: Introductionmentioning

confidence: 99%

Benchmarking Analysis of the Accuracy of Classification Methods Related to Entropy

Orenes

Rabasa

Rodríguez-Sala

et al. 2021

Entropy

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In the machine learning literature we can find numerous methods to solve classification problems. We propose two new performance measures to analyze such methods. These measures are defined by using the concept of proportional reduction of classification error with respect to three benchmark classifiers, the random and two intuitive classifiers which are based on how a non-expert person could realize classification simply by applying a frequentist approach. We show that these three simple methods are closely related to different aspects of the entropy of the dataset. Therefore, these measures account somewhat for entropy in the dataset when evaluating the performance of classifiers. This allows us to measure the improvement in the classification results compared to simple methods, and at the same time how entropy affects classification capacity. To illustrate how these new performance measures can be used to analyze classifiers taking into account the entropy of the dataset, we carry out an intensive experiment in which we use the well-known J48 algorithm, and a UCI repository dataset on which we have previously selected a subset of the most relevant attributes. Then we carry out an extensive experiment in which we consider four heuristic classifiers, and 11 datasets.

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Multiclass EEG signal classification utilizing Rényi min-entropy-based feature selection from wavelet packet transformation

Cited by 36 publications

References 38 publications

The classification of motor imagery response: an accuracy enhancement through the ensemble of random subspace k-NN

The classification of motor imagery response: an accuracy enhancement through the ensemble of random subspace k-NN

Global research on artificial intelligence-enhanced human electroencephalogram analysis

Benchmarking Analysis of the Accuracy of Classification Methods Related to Entropy

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