Effective Classification of EEG Signals Using K-Nearest Neighbor Algorithm

Awan, Umer I.; Rajput, U.H.; Syed, Ghazaal; Iqbal, Rimsha; Sabat, Ifra; Mansoor, Muhammad

doi:10.1109/fit.2016.030

Cited by 17 publications

(8 citation statements)

References 8 publications

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“…The KNN was found to perform effectively to extract and classify feature vector for different facial movements and expressions measured by noninvasive EEG devices. The accuracy was around 98% driven by implementing segmentation to the complete signal waveform [ 29 ]. Even though classification could be applied using other EEG features like the average spectral centroid, average standard deviation, or average energy entropy, but still the power spectral density offers the highest accuracy with all classifiers and was found to score 100% with KNN when analyzing EEG signals from different human cognitive states employed to control brain computer interface (BCI) devices [ 30 ].…”

Section: Discussionmentioning

confidence: 99%

Classification of Lactate Level Using Resting-State EEG Measurements

Shaban

Uçan

Duru

2021

Applied Bionics and Biomechanics

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The electroencephalography (EEG) signals have been used widely for studying the brain neural information dynamics and behaviors along with the developing impact of using the machine and deep learning techniques. This work proposes a system based on the fast Fourier transform (FFT) as a feature extraction method for the classification of human brain resting-state electroencephalography (EEG) recorded signals. In the proposed system, the FFT method is applied on the resting-state EEG recordings and the corresponding band powers were calculated. The extracted relative power features are supplied to the classification methods (classifiers) as an input for the classification purpose as a measure of human tiredness through predicting lactate enzyme level, high or low. To validate the suggested method, we used an EEG dataset which has been recorded from a group of elite-level athletes consisting of two classes: not tired, the EEG signals were recorded during the resting-state task before performing acute exercise and tired, the EEG signals were recorded in the resting-state after performing an acute exercise. The performance of three different classifiers was evaluated with two performance measures, accuracy and precision values. The accuracy was achieved above 98% by the K-nearest neighbor (KNN) classifier. The findings of this study indicated that the feature extraction scheme has the ability to classify the analyzed EEG signals accurately and predict the level of lactate enzyme high or low. Many studying fields, like the Internet of Things (IoT) and the brain computer interface (BCI), can utilize the findings of the proposed system in many crucial decision-making applications.

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Section: Discussionmentioning

confidence: 99%

Classification of Lactate Level Using Resting-State EEG Measurements

Shaban

Uçan

Duru

2021

Applied Bionics and Biomechanics

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“…K nearest neighbor algorithm is a technique that takes a dummy variable to distinguish the signal in various groups. The result is determined by the number of votes cast by its neighbors, that is one of the several reasons of the its name K-nearest neighbor [ 34 ].…”

Section: Proposed Approachmentioning

confidence: 99%

“…This step is essential to distinguish between seizure itself-the ictal period-and the normal non-ictal period. Several algorithms have been used as a classifier such as artificial neural network (ANN) [31], support vector machine (SVM) [32], ensemble [33], K-nearest neighbors (KNN) [34,35], linear discriminant analysis (LDA) [36,37], logistic regression [38], decision tree [39], and Naïve Bayes [40,41].…”

Section: Related Workmentioning

confidence: 99%

Automated seizure diagnosis system based on feature extraction and channel selection using EEG signals

et al. 2021

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Seizure is an abnormal electrical activity of the brain. Neurologists can diagnose the seizure using several methods such as neurological examination, blood tests, computerized tomography (CT), magnetic resonance imaging (MRI) and electroencephalogram (EEG). Medical data, such as the EEG signal, usually includes a number of features and attributes that do not contains important information. This paper proposes an automatic seizure classification system based on extracting the most significant EEG features for seizure diagnosis. The proposed algorithm consists of five steps. The first step is the channel selection to minimize dimensionality by selecting the most affected channels using the variance parameter. The second step is the feature extraction to extract the most relevant features, 11 features, from the selected channels. The third step is to average the 11 features extracted from each channel. Next, the fourth step is the classification of the average features using the classification step. Finally, cross-validation and testing the proposed algorithm by dividing the dataset into training and testing sets. This paper presents a comparative study of seven classifiers. These classifiers were tested using two different methods: random case testing and continuous case testing. In the random case process, the KNN classifier had greater precision, specificity, positive predictability than the other classifiers. Still, the ensemble classifier had a higher sensitivity and a lower miss-rate (2.3%) than the other classifiers. For the continuous case test method, the ensemble classifier had higher metric parameters than the other classifiers. In addition, the ensemble classifier was able to detect all seizure cases without any mistake.

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“…Awan et al [21] have discussed the necessity for classification and feature extraction from facial emotions and movements registered using EEG signals. In this paper, the K-Nearest Neighbor Algorithm has been proposed.…”

Section: K-nearest Neighbor (K-nn) For Eeg Classificationmentioning

confidence: 99%

Survey for Electroencephalography EEG Signal Classification Approaches

Al-Fraiji

Al‐Shammary

2021

Mobile Computing and Sustainable Informatics

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This paper presents a literature survey for electroencephalogram (EEG) signal classification approaches based on machine learning algorithms. EEG classification plays a vital role in many health applications using machine learning algorithms. Mainly, they group and classify patient signals based on learning and developing specific features and metrics. In this paper, 32 highly reputed research publications are presented focusing on the designed and implemented approach, applied dataset, their obtained results and applied evaluation. Furthermore, a critical analysis and statement are provided for the surveyed papers and an overall analysis in order to have all the papers under an evaluation comparison. SVM, ANN, KNN, CNN, LDA, multi-classifier and more other classification approaches are analyzed and investigated. All classification approaches have shown potential accuracy in classifying EEG signals. Evidently, ANN has shown higher persistency and performance than all other models with 97.6% average accuracy.

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Effective Classification of EEG Signals Using K-Nearest Neighbor Algorithm

Cited by 17 publications

References 8 publications

Classification of Lactate Level Using Resting-State EEG Measurements

Classification of Lactate Level Using Resting-State EEG Measurements

Automated seizure diagnosis system based on feature extraction and channel selection using EEG signals

Survey for Electroencephalography EEG Signal Classification Approaches

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