Informatics Publishing Limited

V. V. Ramalingam ¹, S. Mohan ², V. Sugumaran ³

Affiliations
1 Bharathiar University, Coimbatore - 641 046, Tamil Nadu, IN
2 AL Yamamah University, Riyadh, Kingdom of Saudi Arabia, SA
3 VIT University, Chennai Campus, Chennai - 600048, Tamil Nadu, IN

Abstract

Objectives: In monitoring brain activities, Electroencephalogram (EEG) signals play a significant role. As brain activities are many and highly dynamic in nature, processing of EEG signals is a challenging task. Since classification is more accurate when the pattern is simplified through representation by well performing features, feature extraction and selection play an important role in classification systems such as Clonal Selection Classification Algorithm (CSCA) algorithm. Methods/Analysis: This study is one such attempt to perform the prosthetic limb movements using EEG signals. In this research, the performance of CSCA for prosthetic limb movements of EEG signals has been reported. Findings: In this paper, the EEG signals are acquired for four different limb movements like finger open (fopen), finger close (fclose), wrist clockwise (wcw) and wrist counterclock wise (wccw). These EEG signals can be used to build a model to control the prosthetic limb movements using CSCA algorithm. The statistical parameters were extracted from the EEG signals. The best feature set was identified using J48 decision tree classifier. The well performing features were then classified using CSCA algorithm. The classification performance of CSCA has been reported. Novelty/Improvement: Our work is useful for controlling artificial limb with movements using EEG signals. The signal processing of EEG signals is a complex task and requires sophisticated techniques to yield a better classification accuracy.

Keywords

CSCA, Classification, Electroencephalogram (EEG) Signals, Statistical Features

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V. V. Ramalingam ¹, S. Mohan ², B. Rebecca Jeya Vadhanam ³, V. Sugumaran ⁴

Affiliations
1 Department of Computer Science and Engineering, S. R. M University, Kattankulathur – 603203, Tamil Nadu, IN
2 CCIS, AL Yamamah University, Riyadh, Kingdom of Saudi Arabia
3 Department of Computer Applications, Faculty of Science and Humanities, S. R. M University, Kattankulathur – 603203, Tamil Nadu, IN
4 School of Mechanical and Building Sciences (SMBS), VIT University, Chennai Campus, Chennai-600127, Tamil Nadu,, IN

Abstract

Objectives: This paper presented a novel approach with a set of Auto Regressive Moving Average (ARMA) features for the best classification of different hand moments in Electroencephalogram (EEG) signals using C4.5 Decision tree algorithm. Methods/Analysis: The characteristics of EEG signals can be represented through the best features is the most prominent and significant role in the classification systems. The classification is more flawless when the specimen is streamlined through the feature extraction and feature selection process. Findings: In this study, there are four kinds of EEG signals recorded from strong volunteers with finger open, finger close, wrist clockwise and wrist counterclockwise. The well performing statistical features are acquired from the EEG signals. C4.5 Decision tree classifier is used to identify the changes in the EEG signals. The yield of the classifier confirmed that the proposed C4.5 Decision tree classifier has potential to classify the EEG signals of the specific hand movements. Improvement: The proposed work is contributed to manage the right hand movements through the EEG signals. The efficient techniques are required to process the complex EEG signals to achieve the better classification result. To improve the classification accuracy, an efficient feature extraction technique may be applied.

Keywords

ARMA Features, C4.5 Decision Tree, Classification, Electroencephalogram (EEG) Signals.

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V. V. Ramalingam ¹, A. Pandian ¹, R. Parivel ¹

Affiliations
1 Department of Computer Science and Engineering, SRM University, Kattankulathur, Chennai - 603203, Tamil Nadu, IN

Abstract

Objectives: We mainly focus the application of machine learning for artificial limb movement system using Electroencephalogram (EEG) signals. Analysis: EEG signals depict the neuronal activity happening in brain, which will be used to control the artificial limb movement system. Findings: In this paper, four classes of EEG signals were recorded from healthy subjects while performing actions such as finger open (fopen), finger close (fclose), wrist clockwise (wcw) and wrist counterclockwise (wccw) movements. The main objective of this study is to extract the statistical features from EEG signals and identify the best possible features and classify them using J48 Decision Tree algorithm. Improvements: The EEG signals are complex in nature and machine-learning approach was used to study the same. To improve the classification accuracy better feature extraction techniques might be used.

Keywords

Electroencephalogram (EEG) Signals, J48 Algorithm, Statistical Features.

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A. Pandian ¹, V. V. Ramalingam ¹, R. P Vishnu Preet ¹

Affiliations
1 Department of Computer Science and Engineering, SRM University, Kattankulathur, Chennai - 603203, Tamil Nadu, IN

Abstract

Objectives: To training classifier based on the features extracted from the poems of Mukkoodar Pallu, authors for various unknown poems can be classified. Methods/Analysis: The classification accuracy by performing classification in the dataset using C4.5 algorithm is illustrated in this paper. Findings: The results of performing classification on dataset that consists of features extracted from the dataset are shown in this paper. Features like number of characters, number of sentences and the classification accuracy when C4.5 algorithm is used is illustrated. Novelty/Improvement: By doing this, authors of various other poems in Tamil language can be identified which will be helpful to the society. Also a generalized authorship identification tool for all regional languages can be achieved.

Keywords

Authorship, Classification, Feature Selection, Tamil Articles.

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Pandian ¹, V. V. Ramalingam ¹, R. P. Vishnu Preet ¹

Affiliations
1 Department of Computer Science and Engineering, SRM University, Kattankulathur, Chennai - 603203, Tamil Nadu, IN

Abstract

Objective: To classify the authors of unknown Tamil dataset based on the work of known authors. Methods/Analysis: Text processing is the method of deriving high quality information from text that includes statistical patterns from the text. This paper proposes text processing method to extract features and perform classification on the same. Findings: The accuracy of the classifier turns out to be 94.1%. Classifier accuracy is improved from 88.23% to 94.1% by varying the classification algorithm (Bayes Net). Novelty/Improvement: This method can be further extended to all regional languages. By doing this, authors of various other poems in Tamil language can be identified which will be helpful to the society.

Keywords

Authorship, Classification, Feature Selection, Tamil Articles.

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