Quantization-Based Novel Extraction Method Of EEG Signal For Classification (original) (raw)
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Classification of EEG Signals Based on Pattern Recognition Approach
Frontiers in computational neuroscience, 2017
Feature extraction is an important step in the process of electroencephalogram (EEG) signal classification. The authors propose a "pattern recognition" approach that discriminates EEG signals recorded during different cognitive conditions. Wavelet based feature extraction such as, multi-resolution decompositions into detailed and approximate coefficients as well as relative wavelet energy were computed. Extracted relative wavelet energy features were normalized to zero mean and unit variance and then optimized using Fisher's discriminant ratio (FDR) and principal component analysis (PCA). A high density EEG dataset validated the proposed method (128-channels) by identifying two classifications: (1) EEG signals recorded during complex cognitive tasks using Raven's Advance Progressive Metric (RAPM) test; (2) EEG signals recorded during a baseline task (eyes open). Classifiers such as, K-nearest neighbors (KNN), Support Vector Machine (SVM), Multi-layer Perceptron (ML...
Novel Algorithm for Feature Extraction and Classification of EEG signals
This paper aims in developing an algorithm for feature extraction by using Discrete wavelet transform (DWT).Feature extraction from Electroencephalogram (EEG) signal for emotion recognition provides an adequate information. In this paper DWT is used to extract significant features representing emotion in Brain Computer Interface (BCI) in EEG signals. The EEG signals are acquired in real time using Neurosky Mind wave sensor and processed in real time using wavelets for feature extraction. For a given EEG signal brain waves are identified from DWT Spectrum. These brain waves quantify emotions. The proposed algorithm based on DWT, is modeled in Matlab and it is validated using 10 different EEG samples. Features such as energy are found to identify the intensity level of different bands of EEG signal .The best results were obtained by using Bior 5.5 wavelet for signal decomposition and to obtain the accurate frequency bands.
2019
Classification of EEG signal for Brain-Computer Interface (BCI) applications consists of three stages: Preprocessing; Feature extraction and Classification. There are different methods implemented in these stages found in existing literature. However, the performance of the methods has been measured on different datasets which made the results incomparable to each other. To address this problem, in this paper, different combination of feature extraction and classification methods has been implemented to classify a well known dataset (dataset 2A, BCI Competition IV) so that a comparative analysis can be made based on identical platform to find out the best combination of methods. In the pre-processing step, the EEG data was band-pass filtered to remove the artifacts and Common Spatial Pattern (CSP) was applied to increase the discriminativity of the data. Two types of features: Time Domain Parameters (TDP) and Adaptive Auto-Regressive (AAR) parameters were extracted from the pre-proc...
Feature Extraction And Classification Of Eeg Signals Using Neural Network
The use of Electroencephalogram (EEG) or "brain waves" for human-computer interaction is a new and challenging field that has gained momentum in the past few years. In this work different finite impulse response filter (FIR) windowing techniques (Rectangular, Hamming, Hanning, Blackman, Kaiser β= 5,8,12) are used to extract EEG signal to its basic components (Delta wave, Theta wave, Alpha wave, Gamma and Beta wave).The comparison between these windowing methods are done by computing the Fourier transform, power spectrum, SNR values. The features are extracted from the data and applied to classification techniques to identify the accuracy in obtaining the information of the data. In this research, EEG from one subject who performed four tasks has been classified using Radial Basis Function (RBF) and Multi Layer Perceptron (MLP) neural networks. Five data sets with 1000 samples are chosen in order to perform classification techniques. 200 iterations are done to identify the best error rate. These iterations help us to achieve best output. We calculate the elapsed time, confusion matrix, sensitivity, precision, specificity and accuracy for the classified data. The best classification accuracy is approximately 99.66% using the Multi Layer Perceptron technique and the best windowing technique obtained is Kaiser β= 12. The experimental results are performed using MATLAB Tool.
Improved EEG event classification using differential energy
2015 IEEE Signal Processing in Medicine and Biology Symposium (SPMB), 2015
Feature extraction for automatic classification of EEG signals typically relies on time frequency representations of the signal. Techniques such as cepstral-based filter banks or wavelets are popular analysis techniques in many signal processing applications including EEG classification. In this paper, we present a comparison of a variety of approaches to estimating and postprocessing features. To further aid in discrimination of periodic signals from aperiodic signals, we add a differential energy term. We evaluate our approaches on the TUH EEG Corpus, which is the largest publicly available EEG corpus and an exceedingly challenging task due to the clinical nature of the data. We demonstrate that a variant of a standard filter bank-based approach, coupled with first and second derivatives, provides a substantial reduction in the overall error rate. The combination of differential energy and derivatives produces a 24% absolute reduction in the error rate and improves our ability to discriminate between signal events and background noise. This relatively simple approach proves to be comparable to other popular feature extraction approaches such as wavelets, but is much more computationally efficient.
A Brain computing system is a communication channel between the human or animal brain and external environment; it's a collaboration in which brain controls a mechanical device as a natural part of its representation of the body. It is a type of communication which practically uses both software and hardware systems for the communication. It's the type of system which provides a new way of communication between non-muscular channels with the external hardware. Basically Brain computing system is broadly divided into two major categories 1) EEG data signal based pattern recognition approach which actually train the particular brain mental stage and machine observe this pattern, on the behalf on this pattern machine further labels mental state of mind using the classification of pattern. 2) The apparent conditioning approach based on the self-recognition of the EEG signal response. In this paper we review some of the classification techniques for first type of brain computing ...
International Journal of Engineering Research and Technology (IJERT), 2014
https://www.ijert.org/features-analysis-of-eeg-signals-using-neural-network-as-classifier-for-diseases-diagnoses-and-function-operation https://www.ijert.org/research/features-analysis-of-eeg-signals-using-neural-network-as-classifier-for-diseases-diagnoses-and-function-operation-IJERTV3IS080762.pdf Image processing and neural network in today's world grabs massive attentions as it leads to possibilities of broaden application in many fields of high technology. Here will discuss the use of both in the field of biomedical engineering. "A brain computer interface is a communication system that does not depend on the brains normal output pathways of peripheral nerves and muscles" [1]. Brain Computer Interface (BCI) allow disabled people or people who have brain injury or brain diseases to monitor the brain activity. Faster computers and better EEG devices offered new possibilities. There are two main approaches in BCI, the first approach is called operant conditioning approach and the second is of our important is called pattern recognition approach. In the case of pattern recognition approach we will record the EEG wave as a graphs after amplification and filtering and use image processing technique to extract features from these data and then use neural network techniques to make classification and training on these data and then perform test on the system.
FEATURES EXTRACTION TECHNIQES OF EEG SIGNAL FOR BCI APPLICATIONS
The use of Electroencephalogram (EEG) signals in the field of Brain Computer Interface (BCI) have obtained a lot of interest with diverse applications ranging from medicine to entertainment. In this paper, BCI is designed using electroencephalogram (EEG) signals where the subjects have to think of only a single mental task. EEG signals are recorded from 16 channels and studied during several mental and motor tasks. Features are extracted from those signals using several methods: Time Analysis, Frequency Analysis, Time-Frequency Analysis and Time-Frequency-Space Analysis. Extracted EEG features are classified using an artificial neural network trained with the back propagation algorithm. Classification rates that reach 99% between two tasks and 96% between three tasks using Space-Time-Frequency-analysis and Time-Frequency-analysis were obtained.
EEG Signal classification by using Empirical Mode Decomposition and LVQ
2017
An Electroencephalogram (EEG) is a test used to find problems related to electrical activity of the brain. An EEG tracks and records brain wave patterns. An EEG can be used to help detect potential problems associated with this activity. In this paper we propose a new prototype for feature extraction method couple with a Kohonen’s neural network based classifier for classification of EEG signal. EEG signal is decomposed into intrinsic mode function (IMFs) by Emperical mode decomposition algorithm. Using these seven IMFs, six statistical parameter are calculated and forty two features are extracted for classification of EEG signal. This is the input feature vector of the Learning vector quantization classifier. Learning Vector Quantization (LVQ) method which classifies the EEG signal into binary categories: Set A normal human beings with eyes open with Set-B being the data of regular human being in eyes closed condition, Set -C which was taken from the hippocampal formation of the op...
IJERT-Novel Algorithm for Feature Extraction and Classification of EEG signals
International Journal of Engineering Research and Technology (IJERT), 2016
https://www.ijert.org/novel-algorithm-for-feature-extraction-and-classification-of-eeg-signals https://www.ijert.org/research/novel-algorithm-for-feature-extraction-and-classification-of-eeg-signals-IJERTV4IS120299.pdf This paper aims in developing an algorithm for feature extraction by using Discrete wavelet transform (DWT).Feature extraction from Electroencephalogram (EEG) signal for emotion recognition provides an adequate information. In this paper DWT is used to extract significant features representing emotion in Brain Computer Interface (BCI) in EEG signals. The EEG signals are acquired in real time using Neurosky Mind wave sensor and processed in real time using wavelets for feature extraction. For a given EEG signal brain waves are identified from DWT Spectrum. These brain waves quantify emotions. The proposed algorithm based on DWT, is modeled in Matlab and it is validated using 10 different EEG samples. Features such as energy are found to identify the intensity level of different bands of EEG signal .The best results were obtained by using Bior 5.5 wavelet for signal decomposition and to obtain the accurate frequency bands.