2021
DOI: 10.7717/peerj-cs.374
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Abstract: 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… Show more

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Cited by 25 publications
(6 citation statements)
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“…The preprocessed data has been fed into the model to train, and then the model performance has been tested in terms of accuracy and other relevant metrics. Accuracy itself can't guarantee the reliability of this model, so relevant parameters like positive rate, false-negative rate, F1 score are also considered in this type of classification task [35]- [38]. The equations regarding performance parameters have been mentioned in (2) to (5).…”
Section: Resultsmentioning
confidence: 99%
“…The preprocessed data has been fed into the model to train, and then the model performance has been tested in terms of accuracy and other relevant metrics. Accuracy itself can't guarantee the reliability of this model, so relevant parameters like positive rate, false-negative rate, F1 score are also considered in this type of classification task [35]- [38]. The equations regarding performance parameters have been mentioned in (2) to (5).…”
Section: Resultsmentioning
confidence: 99%
“…The processed datasets were incorporated into the developed deep learning methods in forecasting lane markings on roads, and the result was assessed in terms of accuracy. Since accuracy is not the only reliable performance metric for evaluating research performance, other performance metrics such as false positive, false negative, and F1 score can also provide a reliable result for evaluating research work performance [23]- [28]. The performance parameter equations were stated in ( 4)- (7).…”
Section: Resultsmentioning
confidence: 99%
“…Due to the perceptive nature of the input selection of k-NN, ensemble systems based on random subspaces are capable of enhancing the efficiency of single k-NN classifiers [39]. Random subspace is a frequently utilized ensemble technique that generates individual classifiers from randomly chosen subspaces of data [40]. Additionally, the output of each independent classifier is eventually integrated using a conventional majority vote to produce the final outcome.…”
Section: Ensemble Of Random Subspace Knnmentioning
confidence: 99%