Classification of EEG signals for epileptic seizures using Levenberg-Marquardt algorithm based Multilayer Perceptron Neural Network

Olive cultivation over the past few years has spread across Mediterranean countries with Spain being the world’s largest olive producer among them. Because olives are a major part of the economy for such countries keeping records of their tree count and crop yield is of high significance. Manual counting of trees over such large areas is humanly infeasible. To address this problem, we propose an automatic method for the detection and enumeration of olive trees. The algorithm is a multi-step classification system comprising pre-processing, image segmentation, feature extraction, and classification. RGB satellite images were acquired from the Spanish territory and pre-processed to suppress the additive noise. The region of interest was then segmented from the pre-processed images using K-Means segmentation, through which statistical features were extracted and classified. Promising results were achieved for all classifiers, namely Naive Bayesian, Support Vector Machines (SVMs), Random Forest and Multi-Layer Perceptrons (MLPs), at various division ratios of data samples. In a comparison of all the classification algorithms, Random Forest outperformed the rest by an overall accuracy of 97.5% at the division ratio of 70 to 30 for training to testing.

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“…Transfer function outputs the transformed signal to other nodes. In MLP, all nodes and layers are arranged in a feed-forward manner [25].…”

Section: Multi-layer Perceptron (Mlp)mentioning

confidence: 99%

Automatic Detection System of Olive Trees Using Improved K-Means Algorithm

Waleed

Khan

et al. 2020

Remote Sensing

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“…Finally, MLP, also called an artificial neural network, is a kind of neural network method which is often used in various fields, including EEG signal classification [34,35]. The MLP model consists of an input layer, an output layer, and multiple hidden layers.…”

Section: Related Workmentioning

confidence: 99%

A Feature Extraction Method Based on Differential Entropy and Linear Discriminant Analysis for Emotion Recognition

Chen

Miao

Yang

et al. 2019

Sensors

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Feature extraction of electroencephalography (EEG) signals plays a significant role in the wearable computing field. Due to the practical applications of EEG emotion calculation, researchers often use edge calculation to reduce data transmission times, however, as EEG involves a large amount of data, determining how to effectively extract features and reduce the amount of calculation is still the focus of abundant research. Researchers have proposed many EEG feature extraction methods. However, these methods have problems such as high time complexity and insufficient precision. The main purpose of this paper is to introduce an innovative method for obtaining reliable distinguishing features from EEG signals. This feature extraction method combines differential entropy with Linear Discriminant Analysis (LDA) that can be applied in feature extraction of emotional EEG signals. We use a three-category sentiment EEG dataset to conduct experiments. The experimental results show that the proposed feature extraction method can significantly improve the performance of the EEG classification: Compared with the result of the original dataset, the average accuracy increases by 68%, which is 7% higher than the result obtained when only using differential entropy in feature extraction. The total execution time shows that the proposed method has a lower time complexity.

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“…The overview of the state-of-the-art methods given above indicates that most of the existing methods do not give satisfactory performance for classification of EEG signals. Moreover, the existing methods are application specific and work only on the pre-processed EEG signals [7,[26][27]54]. Further, these techniques suffer from overfitting problem; when they are applied on different dataset concerning the same problem, the classification rate [29].…”

Section: Literature Reviewmentioning

confidence: 99%

An efficient intelligent system for the classification of electroencephalography (EEG) brain signals using nuclear features for human cognitive tasks

Qazi

Hussain

Aboalsamh

2019

IFS

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Representation and classification of Electroencephalography (EEG) brain signals are critical processes for their analysis in cognitive tasks. Particularly, extraction of discriminative features from raw EEG signals, without any pre-processing, is a challenging task. Motivated by nuclear norm, we observed that there is a significant difference between the variances of EEG signals captured from the same brain region when a subject performs different tasks. This observation lead us to use singular value decomposition for computing dominant variances of EEG signals captured from a certain brain region while performing a certain task and use them as features (nuclear features). A simple and efficient class means based minimum distance classifier (CMMDC) is enough to predict brain states. This approach results in the feature space of significantly small dimension and gives equally good classification results on clean as well as raw data. We validated the effectiveness and robustness of the technique using four datasets of different tasks: fluid intelligence clean data (FICD), fluid intelligence raw data (FIRD), memory recall task (MRT), and eyes open / eyes closed task (EOEC). For each task, we analyzed EEG signals over six (06) different brain regions with 8,16,20,18,18 and 100 electrodes. The nuclear features from frontal brain region gave the 100% prediction accuracy. The discriminant analysis of the nuclear features has been conducted using intra-class and inter-class variations. Comparisons with the state-of-the-art techniques showed the superiority of the proposed system.

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Classification of EEG signals for epileptic seizures using Levenberg-Marquardt algorithm based Multilayer Perceptron Neural Network

Cited by 25 publications

References 11 publications

Automatic Detection System of Olive Trees Using Improved K-Means Algorithm

Automatic Detection System of Olive Trees Using Improved K-Means Algorithm

A Feature Extraction Method Based on Differential Entropy and Linear Discriminant Analysis for Emotion Recognition

An efficient intelligent system for the classification of electroencephalography (EEG) brain signals using nuclear features for human cognitive tasks

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