CLASSIFICATION OF EEG-BASED HAND GRASPING IMAGINATION USING AUTOREGRESSIVE AND NEURAL NETWORKS

2015 ◽  
Vol 78 (6-6) ◽  
Author(s):  
Esmeralda Contessa Djamal ◽  
Suprijanto Suprijanto ◽  
Steven J. Setiadi
Keyword(s):  
Neural Networks ◽  
Classification System ◽  
Brain Computer Interface ◽  
Small Error ◽  
Training Data ◽  
Ar Model ◽  
Computer Interface ◽  
Eeg Signal ◽  
The Mind

In the development of Brain Computer Interface (BCI), one important issue is the classification of hand grasping imagination. It is helpful for realtime control of the robotic or a game of the mind. BCI uses EEG signal to get information on the human. This research proposed methods to classify EEG signal against hand grasping imagination using Neural Networks.  EEG signal was recorded in ten seconds of four subjects each four times that were asked to imagine three classes of grasping (grasp, loose, and relax). Four subjects used as training data and four subjects as testing data. First, EEG signal was modeled in order 20 Autoregressive (AR) so that got AR coefficients being passed Neural Networks. The order of the AR model chosen based optimization gave a small error that is 1.96%. Then, it has developed a classification system using multilayer architecture and Adaptive Backpropagation as training algorithm. Using AR made training of the system more stable and reduced oscillation. Besides, the use of the AR model as a representation of the EEG signal improved the classification system accuracy of 68% to 82%. To verify the performance improvement of the proposed classification scheme, a comparison of the Adaptive Backpropagation and the conventional Backpropagation in training of the system. It resulted in an increase accuracy of 76% to 82%. The system was validated against all training data that produced an accuracy of 91%. The classification system that has been implemented in the software so that can be used as the brain computer interface.  

scholarly journals Brain-Computer Interface for Control of Wheelchair Using Fuzzy Neural Networks

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Rahib H. Abiyev ◽  
Nurullah Akkaya ◽  
Ersin Aytac ◽  
Irfan Günsel ◽  
Ahmet Çağman
Keyword(s):  
Neural Networks ◽  
Brain Activity ◽  
Brain Computer Interface ◽  
Fuzzy Neural Networks ◽  
Training Data ◽  
Computer Interface ◽  
Human Control ◽  
Fuzzy Neural ◽  
Physically Disabled People

The design of brain-computer interface for the wheelchair for physically disabled people is presented. The design of the proposed system is based on receiving, processing, and classification of the electroencephalographic (EEG) signals and then performing the control of the wheelchair. The number of experimental measurements of brain activity has been done using human control commands of the wheelchair. Based on the mental activity of the user and the control commands of the wheelchair, the design of classification system based on fuzzy neural networks (FNN) is considered. The design of FNN based algorithm is used for brain-actuated control. The training data is used to design the system and then test data is applied to measure the performance of the control system. The control of the wheelchair is performed under real conditions using direction and speed control commands of the wheelchair. The approach used in the paper allows reducing the probability of misclassification and improving the control accuracy of the wheelchair.

scholarly journals Emotion brain-computer interface using wavelet and recurrent neural networks

2020 ◽  
Vol 6 (1) ◽  
pp. 1 ◽  
Author(s):  
Esmeralda Contessa Djamal ◽  
Hamid Fadhilah ◽  
Asep Najmurrokhman ◽  
Arlisa Wulandari ◽  
Faiza Renaldi
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Short Term Memory ◽  
Brain Computer Interface ◽  
Computing Time ◽  
Extraction Methods ◽  
Training Data ◽  
Computer Interface ◽  
Eeg Signal ◽  
Eeg Signals

Brain-Computer Interface (BCI) has an intermediate tool that is usually obtained from EEG signal information. This paper proposed the BCI to control a robot simulator based on three emotions for five seconds by extracting a wavelet function in advance with Recurrent Neural Networks (RNN). Emotion is amongst variables of the brain that can be used to move external devices. BCI's success depends on the ability to recognize one person’s emotions by extracting their EEG signals. One method to appropriately recognize EEG signals as a moving signal is wavelet transformation. Wavelet extracted EEG signal into theta, alpha, and beta wave, and consider them as the input of the RNN technique. Connectivity between sequences is accomplished with Long Short-Term Memory (LSTM). The study also compared frequency extraction methods using Fast Fourier Transform (FFT). The results showed that by extracting EEG signals using Wavelet transformations, we could achieve a confident accuracy of 100% for the training data and 70.54% of new data. While the same RNN configuration without pre-processing provided 39% accuracy, even adding FFT would only increase it to 52%. Furthermore, by using features of the frequency filter, we can increase its accuracy from 70.54% to 79.3%. These results showed the importance of selecting features because of RNNs concern to sequenced its inputs. The use of emotional variables is still relevant for instructions on BCI-based external devices, which provide an average computing time of merely 0.235 seconds.

scholarly journals A LabVIEW Instrument Aimed for the Research on Brain-Computer Interface by Enabling the Acquisition, Processing, and the Neural Networks based Classification of the Raw EEG Signal Detected by the Embedded NeuroSky Biosensor

2022 ◽  
Author(s):  
Oana Andreea Rușanu
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Brain Computer Interface ◽  
Automatic Generation ◽  
Computer Interface ◽  
Eeg Signal ◽  
Eye Blink ◽  
Eye Blinks ◽  
Temporal Sequences

This paper proposes several LabVIEW applications to accomplish the data acquisition, processing, features extraction and real-time classification of the electroencephalographic (EEG) signal detected by the embedded sensor of the NeuroSky Mindwave Mobile headset. The LabVIEW applications are aimed at the implementation of a Brain-Computer Interface system, which is necessary to people with neuromotor disabilities. It is analyzed a novel approach regarding the preparation and automatic generation of the EEG dataset by identifying the most relevant multiple mixtures between selected EEG rhythms (both time and frequency domains of raw signal, delta, theta, alpha, beta, gamma) and extracted statistical features (mean, median, standard deviation, route mean square, Kurtosis coefficient and others). The acquired raw EEG signal is processed and segmented into temporal sequences corresponding to the detection of the multiple voluntary eye-blinks EEG patterns. The main LabVIEW application accomplished the optimal real-time artificial neural networks techniques for the classification of the EEG temporal sequences corresponding to the four states: 0 - No Eye-Blink Detected; 1 - One Eye-Blink Detected; 2 – Two Eye-Blinks Detected and 3 – Three Eye-Blinks Detected. Nevertheless, the application can be used to classify other EEG patterns corresponding to different cognitive tasks, since the whole functionality and working principle could estimate the labels associated with various classes.

scholarly journals Design of a general brain-computer interface

2011 ◽  
Vol 22 (6) ◽  
pp. 638-646 ◽  
Author(s):  
Alessandro B. Benevides ◽  
Mário Sarcinelli-Filho ◽  
Teodiano F. Bastos Filho
Keyword(s):  
Feature Extraction ◽  
Brain Computer Interface ◽  
Sampling Frequency ◽  
Computer Interface ◽  
Eeg Signal ◽  
Time Process ◽  
Class Separation ◽  
Power Spectral ◽  
Mental Tasks

This paper presents the classification of three mental tasks, using the EEG signal and simulating a real-time process, what is known as pseudo-online technique. The Bayesian classifier is used to recognize the mental tasks, the feature extraction uses the Power Spectral Density, and the Sammon map is used to visualize the class separation. The choice of the EEG channel and sampling frequency is based on the Kullback-Leibler symmetric divergence and a reclassification model is proposed to stabilize the classifications.

scholarly journals A Brief Review on EEG Signal Pre-processing Techniques for Real-Time Brain-Computer Interface Applications

2021 ◽  
Author(s):  
B Venkata Phanikrishna ◽  
Paweł Pławiak ◽  
Allam Jaya Prakash
Keyword(s):  
Deep Learning ◽  
Brain Activity ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Eeg Signal ◽  
Monitoring Method ◽  
Brain Signals ◽  
Processing Techniques

<div>Electro Encephalo Gram (EEG) is a monitoring method used in biomedical and computer science to understand brain activity. Therefore, the analysis and classification of these signals play a prominent role in estimating a person’s behavior to certain events. Manually analyzing these signals is very tedious and time-consuming, so an automated scientific tool is required to analyze the brain signals. In this work, the authors are explored various pre-processing segmentation techniques that are helpful in an automatic machine and deep learning-based classification methods available for EEG signal processing. Most of the machine and deep learning methods are followed pre-processing as a common step in classification. Extraction of the basic sub-band components from EEG signals such as delta (δ), theta (θ), alpha (α), beta (β), and gamma (γ) is very important in the pre-processing stage. These sub bands of EEG signal have extraordinary evidence related to multiple neurophysiological processes, which are useful for further prediction & diagnosis of diseases and other emotion-based applications. This review paper elaborates various elementary ideas of extracting EEG sub-bands and the role of EEG in Brain-Computer Interface (BCI) in the classification. <b> (Submitted To IEEE reviews in Biomedical Engineering)</b></div>

scholarly journals Advanced LabVIEW Applications Aimed for the Acquisition, Processing and Classification of the Electroencephalographic Signals Used in a Brain-Computer Interface System

2021 ◽  
Author(s):  
Oana Andreea Rușanu
Keyword(s):  
Real Time ◽  
Brain Computer Interface ◽  
Automatic Generation ◽  
Computer Interface ◽  
Eeg Signal ◽  
Eye Blink ◽  
Eye Blinks ◽  
Interface System ◽  
Temporal Sequences

This paper proposes several LabVIEW applications to accomplish the data acquisition, processing, features extraction and real-time classification of the electroencephalographic (EEG) signal detected by the embedded sensor of the NeuroSky Mindwave Mobile headset. The LabVIEW applications are aimed at the implementation of a Brain-Computer Interface system, which is necessary to people with neuromotor disabilities. It is analyzed a novel approach regarding the preparation and automatic generation of the EEG dataset by identifying the most relevant multiple mixtures between selected EEG rhythms (both time and frequency domains of raw signal, delta, theta, alpha, beta, gamma) and extracted statistical features (mean, median, standard deviation, route mean square, Kurtosis coefficient and others). The acquired raw EEG signal is processed and segmented into temporal sequences corresponding to the detection of the multiple voluntary eye-blinks EEG patterns. The main LabVIEW application accomplished the optimal real-time artificial neural networks techniques for the classification of the EEG temporal sequences corresponding to the four states: 0 - No Eye-Blink Detected; 1 - One Eye-Blink Detected; 2 &ndash; Two Eye-Blinks Detected and 3 &ndash; Three Eye-Blinks Detected. Nevertheless, the application can be used to classify other EEG patterns corresponding to different cognitive tasks, since the whole functionality and working principle could estimate the labels associated with various classes.

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