Implementation of Neural Network with ALE for the Removal of Artifacts in EEG Signals

2020 ◽  
Vol 15 (1) ◽  
pp. 77-83
Author(s):  
R. Suresh Kumar ◽  
P. Manimegalai
Keyword(s):  
Neural Network ◽  
Low Cost ◽  
Information Source ◽  
Activation Function ◽  
Signal Extraction ◽  
Eeg Signal ◽  
Least Mean Square ◽  
Eeg Signals ◽  
Adaptive Line Enhancer ◽  
Field Programmable

Objective: The EEG signal extraction offers an opportunity to improve the quality of life in patients, which has lost to control the ability of their body, with impairment of locomotion. Electroencephalogram (EEG) signal is an important information source for underlying brain processes. Materials and Methods: The signal extraction and denoising technique obtained through timedomain was then processed by Adaptive Line Enhancer (ALE) to extract the signal coefficient and classify the EEG signals based on FF network. The adaptive line enhancer is used to update the coefficient during the runtime with the help of adaptive algorithms (LMS, RLS, Kalman Filter). Results: In this work, the least mean square algorithm was employed to obtain the coefficient update with respect to the corresponding input signal. Finally, Mat lab and verilog HDL language are used to simulate the signals and got the classification accuracy rate of 80%. Conclusion: Experiments show that this method can get high and accurate rate of classification. In this paper, it is proposed that a low-cost use of Field Programmable Gate Arrays (FPGAs) can be used to process EEG signals for extracting and denoising. As a preliminary study, this work shows the implementation of a Neural Network, integrated with ALE for EEG signal processing. The preliminary tests through the proposed architecture for the activation function shows to be reasonable both in terms of precision and in processing speed.

EEG ENHANCEMENT USING EXTENDED KALMAN FILTER TO TRAIN MULTI-LAYER PERCEPTRON

2019 ◽  
Vol 31 (01) ◽  
pp. 1950005 ◽  
Author(s):  
Manel Yakoubi ◽  
Rachid Hamdi ◽  
Mounir Bousbia Salah
Keyword(s):  
Neural Network ◽  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Brain Activity ◽  
Activation Function ◽  
Eeg Signal ◽  
Mean Square ◽  
Least Mean Square ◽  
Multi Layer Perceptron ◽  
Non Stationary Signal

In many applications of signal processing, especially in biomedicine, electroencephalogram (EEG) is the recording of electrophysiological brain activity along the scalp over a small interval of time and it is a biological non stationary signal which contains important information. Analysis of EEG signal is useful to identify physiological situations of the human as normal and epileptic subject. EEG signal becomes more complicated to be analyzed by the introduction of the noise. In this paper, a nonlinear Kalman Filter scheme where an extended Kalman filter (EKF) based Multi-layer perceptron (MLP) model is proposed to remove white and colored Gaussian noises from EEG recordings in physiological and pathological states (normal and epileptic). The MLP is one of the artificial neural network (ANN) models that has great track of impacts at solving a variety of problems. Activation function is one of the elements in MLP neural network. Selection of the activation function as sigmoid in the MLP network plays an essential role on the network performance. Thus, the MLP parameters as weights, and outputs are trained by an EKF in order to minimize the difference between the output of the neural network and the desired outputs. The results comparison studies are evaluated with root mean square difference (RMSD) and signal to noise ratio (SNR). The elapsed time is decreased using this method compared to normalised least mean square (NLMS) and Meyer wavelet methods. These parameters applied to EEG signals show the validity and effectiveness of the proposed approach.

scholarly journals Mind-Wave Controlled Robot: An Arduino Robot Simulating the Wheelchair for Paralyzed Patients

2018 ◽  
Vol 1 (1) ◽  
pp. 6
Author(s):  
Chi Hang Cheng ◽  
Shuai Li ◽  
Seifedine Kadry
Keyword(s):  
Neural Network ◽  
Low Cost ◽  
Eeg Signal ◽  
The Neural Network ◽  
Lead Electrode ◽  
Short Period ◽  
Specific Position ◽  
And Control

This project attempts to implement an Arduino robot to simulate a brainwave-controlled wheelchair for paralyzed patients with an improved controlling method. The robot should be able to move freely in anywhere under the control of the user and it is not required to predefine any map or path. An accurate and natural controlling method is provided, and the user can stop the robot any time immediately to avoid risks or danger. This project is using a low-cost and a brainwave-reading headset which has only a single lead electrode (Neurosky mind wave headset) to collect the EEG signal. BCI will be developed by sending the EEG signal to the Arduino Mega and control the movement of the robot. This project used the eye blinking as the robot controlling method as the eye blinking will cause a significant pulse in the EEG signal. By using the neural network to classify the blinking signal and the noise, the user can send the command to control the robot by blinking twice in a short period of time. The robot will be evaluated by driving in different places to test whether it can follow the expected path, avoid the obstacles, and stop in a specific position.

Wireless autonomic neural network in EEG signal extraction management

2014 ◽  
Vol 50 (1/2) ◽  
pp. 18
Author(s):  
Chiemela Onunka ◽  
Glen Bright ◽  
Riaan Stopforth
Keyword(s):  
Neural Network ◽  
Signal Extraction ◽  
Eeg Signal

scholarly journals Epileptic Detection from the Eeg Signal using the Anti colony Optimization Technique with Deep Neural Network

2020 ◽  
Vol 9 (1) ◽  
pp. 2726-2733
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Time Series Data ◽  
Optimization Technique ◽  
Signal Decomposition ◽  
Ant Colony ◽  
Series Data ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Mode Decomposition

Extensively used technique to diagnose the epilepsy is EEG. The research objective is to check the variations of frequency found in the epileptic EEG signals.. The EEG dataset were acquired from online database of the Bonn University (BU). Then, butterworth type two filter was implemented to remove the unwanted artifacts from the acquired EEG signals. Further, Multivariate Variational Mode Decomposition (MVMD) methodology was applied to decompose the denoised EEG signals. The signal decomposition helps in finding the necessary information, which required to model the complex time series data. Then, the features were extracted from decomposed signals by using fifteen entropy, linear and statistical features. In addition, ant colony optimization technique was proposed for optimizing the extracted features. The optimized feature vectors were classified by Deep Neural Network (DNN) that includes two circumstances (seizure and healthy), and (Interictal, ictal, and normal). The accuracy attained using the ant colony with deep neural network is 98.12% using the BU EEG dataset, respectively related to the existing models.

scholarly journals NEURAL-NETWORK SEGMENTATION OF ELECTROENCEPHALOGRAM SIGNALS FOR EPILEPTIFORM ACTIVITY DETECTION

2014 ◽  
pp. 30-37
Author(s):  
Svetlana Bezobrazova ◽  
Vladimir Golovko
Keyword(s):  
Neural Network ◽  
Epileptiform Activity ◽  
Eeg Signal ◽  
Network Approach ◽  
Activity Detection ◽  
Eeg Signals ◽  
Neural Network Approach ◽  
Signal Segmentation ◽  
The Neural Network ◽  
Eeg Segmentation

A goal of EEG signals analysis is not only human psychologically and functionality states definition but also pathological activity detection. In this paper we present an approach for epileptiform activity detection by artificial neural network technique for EEG signal segmentation and for the highest Lyapunov’s exponent computing. The EEG segmentation by the neural network approach makes it possible to detect an abnormal activity in signals. We examine our system for segmentation and anomaly detection on the EEG signals where the anomaly is an epileptiform activity.

scholarly journals Single Perceptron Model for Smart Beam forming in Array Antennas

2016 ◽  
Vol 6 (5) ◽  
pp. 2300
Author(s):  
K.S. Senthilkumar ◽  
K. Pirapaharan ◽  
P.R.P Hoole ◽  
R.R.H Hoole
Keyword(s):  
Neural Network ◽  
Single Neuron ◽  
Activation Function ◽  
Adaptive Beamforming ◽  
Least Mean Square ◽  
Radiation Patterns ◽  
Array Antennas ◽  
Smart Beam ◽  
Lms Method ◽  
Selection Of

<p>In this paper, a single neuron neural network beamformer is proposed. A perceptron model is designed to optimize the complex weights of a dipole array antenna to steer the beam to desired directions. The objective is to reduce the complexity by using a single neuron neural network and utilize it for adaptive beamforming in array antennas. The selection of nonlinear activation function plays the pivotal role in optimization depends on whether the weights are real or complex. We have appropriately proposed two types of activation functions for respective real and complex weight values.   The optimized radiation patterns obtained from the single neuron neural network are compared with the respective optimized radiation patterns from the traditional Least Mean Square (LMS) method. Matlab is used to optimize the weights in neural network and LMS method as well as display the radiation patterns.</p>

scholarly journals Simulation design of an Intelligent system for Automotive transmission Gearbox Based on FPGA

2018 ◽  
Vol 6 (2) ◽  
pp. 395-411
Author(s):  
Azzad Bader SAEED
Keyword(s):  
Neural Network ◽  
Intelligent System ◽  
Back Propagation ◽  
Activation Function ◽  
Back Propagation Neural Network ◽  
Speed Ratio ◽  
Simulation Design ◽  
Automotive Transmission ◽  
Field Programmable ◽  
Hidden Layer

In this paper, an artificial  intelligent system has been designed, realized, and downloaded into  FPGA (Field Programmable Gate Array), which is used to control five speed ratio steps ( 1,2,3,4,5) of an electrically controlled type of  automotive transmission gearbox of a vehicle, the first speed ratio step (1) is characterized by the  highest torque, a lowest velocity, while, the  fifth step is characterized by the lowest torque, and highest velocity.The Back-propagation neural network has been used as the intelligent system for the proposed system. The proposed neural network is composed from   eight neurons in the input layer, five neurons in the hidden layer, and five neurons in the output layer. For real downloading into the FPGA, Satlins and Satlin linear activation function has been used for the hidden and output layers respectively. The training function Trainlm ( Levenberg-Marqurdt training) has been used as a learning method for the proposed neural network, which it has a powerful algorithm. The proposed simulation system has been designed and downloaded into the FPGA using MATLAB and ISE Design Suit software packages.

Clinical Diagnosis of Psychiatry Based on Electroencephalography

2021 ◽  
Vol 11 (3) ◽  
pp. 955-963
Author(s):  
Lixue Yuan ◽  
Yinyan Fan ◽  
Quanxi Gan ◽  
Huibin Feng
Keyword(s):  
Environmental Control ◽  
Low Cost ◽  
Training System ◽  
Brain Machine Interface ◽  
Signal Acquisition ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Feedback Information ◽  
Machine Interface ◽  
Neural Feedback

At present, neurophysiological signals used for neuro feedback are EEG (Electroencephalogram), functional magnetic resonance imaging. Among them, the acquisition of EEG signals has the advantages of non-invasive way with low cost. It has been widely used in brain-machine interface technology in recent years. Important progress has been made in rehabilitation and environmental control. However, neural feedback and brainmachine interface technology are completely similar in signal acquisition, signal feature extraction, and pattern classification. Therefore, the related research results of brain-machine interface can be used to closely cooperate with clinical needs to research and develop neural feedback technology based on EEG. Based on neurophysiology and brain-machine interface technology, this paper develops a neural feedback training system based on the acquisition and analysis of human EEG signals. Aiming at the autonomous rhythm components in the EEG signal, such as sensorimotor rhythm and alpha rhythm, the characteristic parameters are extracted through real-time EEG signal processing to generate feedback information, and the subject is self-regulated and trained from a physiological-psychological perspective by providing adjuvant treatment, a practical and stable treatment platform for the clinic.

scholarly journals Implementing Neural Network and Multi resolution analysis in EEG signal for early detection of epilepsy

SCITECH Nepal ◽  
2019 ◽  
Vol 14 (1) ◽  
pp. 8-16 ◽  
Author(s):  
Sachin Shrestha ◽  
Rupesh Dahi Shrestha ◽  
Bhojraj Thapa
Keyword(s):  
Neural Network ◽  
Dimension Reduction ◽  
Epileptic Seizure ◽  
Principal Component ◽  
Epileptic Seizures ◽  
Discrete Wavelet ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Multi Resolution Analysis ◽  
Resolution Analysis

Epilepsy is a neurological disorder of brain and the electroencephalogram (EEG) signals are commonly used to detect the epileptic seizures, the result of abnormal electrical activity in the brain. This paper focuses on the analysis of EEG signal to detect the presence of the epileptic seizure prior to its occurrence. The result could aid the individual in the initiation of delay sensitive diagnostic, therapeutic and alerting procedures. The methodology involves the multi resolution analysis (MRA) of the EEG signals of epileptic patient. MRA is carried out using discrete wavelet transform with daubechies 8 as the mother wavelet. For EEG data, the database of MIT-BIH of seven patients with different cases of epileptic seizure was used. The result with one of the patients showed presence of a unique pattern during the spectral analysis of the signal over different bands. Hence, based on the first patient, similar region is selected with the other patients and the multi-resolution analysis along with the principal component analysis (PCA) for the dimension reduction is carried out. Finally, these are treated with neural network to perform the classification of the signal either the epilepsy is occurring or not. The final results showed 100% accuracy with the detection with the neural network however it uses a large amount of data for analysis. Thus, the same was tested with dimension reduction using PCA which reduced the average accuracy to 89.51%. All the results have been simulated within the Matlab environment.

scholarly journals A Densely Connected Multi-Branch 3D Convolutional Neural Network for Motor Imagery EEG Decoding

2021 ◽  
Vol 11 (2) ◽  
pp. 197
Author(s):  
Tianjun Liu ◽  
Deling Yang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Motor Imagery ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Signal Features ◽  
Average Accuracy ◽  
3D Representation ◽  
3D Cnn ◽  
The Way

Motor imagery (MI) is a classical method of brain–computer interaction (BCI), in which electroencephalogram (EEG) signal features evoked by imaginary body movements are recognized, and relevant information is extracted. Recently, various deep-learning methods are being focused on in finding an easy-to-use EEG representation method that can preserve both temporal information and spatial information. To further utilize the spatial and temporal features of EEG signals, an improved 3D representation of the EEG and a densely connected multi-branch 3D convolutional neural network (dense M3D CNN) for MI classification are introduced in this paper. Specifically, as compared to the original 3D representation, a new padding method is proposed to pad the points without electrodes with the mean of all the EEG signals. Based on this new 3D presentation, a densely connected multi-branch 3D CNN with a novel dense connectivity is proposed for extracting the EEG signal features. Experiments were carried out on the WAY-EEG-GAL and BCI competition IV 2a datasets to verify the performance of this proposed method. The experimental results show that the proposed framework achieves a state-of-the-art performance that significantly outperforms the multi-branch 3D CNN framework, with a 6.208% improvement in the average accuracy for the BCI competition IV 2a datasets and 6.281% improvement in the average accuracy for the WAY-EEG-GAL datasets, with a smaller standard deviation. The results also prove the effectiveness and robustness of the method, along with validating its use in MI-classification tasks.

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