EEG based Directional Signal Classification using RNN Variants

Adhikari, Bikram; Shrestha, Ankit; Mishra, Sabyashachi; Singh, Suyog; Timalsina, Arun Kumar

doi:10.1109/cccs.2018.8586823

Cited by 6 publications

(2 citation statements)

References 16 publications

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“…Deep learning based neural networks were used in [24] for the raw EEG data and the power output classification. 56% accuracy was achieved for EEG raw data and 44.75% accuracy for power data.…”

Section: Related State-of-the-art Techniquesmentioning

confidence: 99%

Predicting the Standard and Deviant Patterns in EEG Signals Based on Deep Learning Model

Sharif

Al-Bayatti

Alfakeeh

2019

2019 International Conference on Computing, Electronics &Amp; Communications Engineering (iCCECE)

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In the recent years, there has been a significant growth in the area of brain computer interference. The main aim of such area is to read the brain activities, formulate a specific/desired output and power a specific approach using such output. Electroencephalography (EEG) may provide an insight into the analysis procedure of the human behavior and the level of the attention. Using the deep learning based neural network has a great success in different applications recently, such as making a decision, classifying a pattern and predicting an outcome by learning from a set of data and build the right weight matrices to represent the prediction outcome or the learning patterns. This research work proposes a novel model based on long short-term memory network to predict the standard and the deviant cases within EEG data sets. The EEG signals are acquired utilizing all the 128 electrodes that represent the 128 channels from infants aged between 5 and 7 months. Statistical approaches, principal component analysis (PCA) and autoregressive (AR) power spectral density estimate have been employed to extract the features from the EEG data sets. The proposed deep learning based model has shown great robustness dealing with different types of features extracted from the processed data sets. Very promising results have been achieved in predicting the standard and deviant cases. The standard case was presented with frequent, repetitive stimulus and the deviant case was presented with infrequent sounds.

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Section: Related State-of-the-art Techniquesmentioning

confidence: 99%

Predicting the Standard and Deviant Patterns in EEG Signals Based on Deep Learning Model

Sharif

Al-Bayatti

Alfakeeh

2019

2019 International Conference on Computing, Electronics &Amp; Communications Engineering (iCCECE)

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“…Some researches have explored the use of LSTM on non-medical EEG-based applications. Most of the EEG-based LSTM applications were used in brain-computer interface (BCI), such as motor imagery classification [ 40 , 41 , 42 , 43 , 44 , 45 , 46 ], emotion classification [ 47 , 48 , 49 , 50 , 51 , 52 ], depression detection [ 53 , 54 , 55 ], biometrics [ 56 , 57 ], sleep stage classification [ 58 , 59 , 60 , 61 , 62 , 63 ], driving behavioral classification [ 64 , 65 ], directional signal classification [ 66 ], machine health monitoring [ 67 ] and EEG signal classification [ 68 ]. There are some research works on LSTM for medical applications reported in the literature [ 69 , 70 , 71 , 72 , 73 , 74 , 75 ], but as far as our concern, there is still no approach being proposed to identify TBI using LSTM networks.…”

Section: Introductionmentioning

confidence: 99%

Classification of Non-Severe Traumatic Brain Injury from Resting-State EEG Signal Using LSTM Network with ECOC-SVM

Lai

Ibrahim

Hamid

et al. 2020

Sensors

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Traumatic brain injury (TBI) is one of the common injuries when the human head receives an impact due to an accident or fall and is one of the most frequently submitted insurance claims. However, it is often always misused when individuals attempt an insurance fraud claim by providing false medical conditions. Therefore, there is a need for an instant brain condition classification system. This study presents a novel classification architecture that can classify non-severe TBI patients and healthy subjects employing resting-state electroencephalogram (EEG) as the input, solving the immobility issue of the computed tomography (CT) scan and magnetic resonance imaging (MRI). The proposed architecture makes use of long short term memory (LSTM) and error-correcting output coding support vector machine (ECOC-SVM) to perform multiclass classification. The pre-processed EEG time series are supplied to the network by each time step, where important information from the previous time step will be remembered by the LSTM cell. Activations from the LSTM cell is used to train an ECOC-SVM. The temporal advantages of the EEG were amplified and able to achieve a classification accuracy of 100%. The proposed method was compared to existing works in the literature, and it is shown that the proposed method is superior in terms of classification accuracy, sensitivity, specificity, and precision.

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Brain-Computer Interface of Motor Imagery Using ICA and Recurrent Neural Networks

Safitri

Djamal

Nugraha

2020

2020 3rd International Conference on Computer and Informatics Engineering (IC2IE)

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EEG based Directional Signal Classification using RNN Variants

Cited by 6 publications

References 16 publications

Predicting the Standard and Deviant Patterns in EEG Signals Based on Deep Learning Model

Predicting the Standard and Deviant Patterns in EEG Signals Based on Deep Learning Model

Classification of Non-Severe Traumatic Brain Injury from Resting-State EEG Signal Using LSTM Network with ECOC-SVM

Brain-Computer Interface of Motor Imagery Using ICA and Recurrent Neural Networks

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