Classification of productive profile using brain signals: Deep learning and neuroscience meets the mining industry

2021 ◽  
Author(s):  
Ana Siravenha ◽  
Walisson Gomes ◽  
Renan Tourinho ◽  
Sergio Viademonte ◽  
Bruno Gomes
Keyword(s):  
Deep Learning ◽  
Iron Ore ◽  
Signal To Noise Ratio ◽  
Mining Industry ◽  
Eeg Signals ◽  
Neural Data ◽  
Technical Staff ◽  
Brain Signals ◽  
Mining Complex

Classification of electroencephalography (EEG) signals is a complex task. EEG is a non-stationary time process with low signal to noise ratio. Among many methods usedfor EEG classification, those based on Deep Learning (DL) have been relatively successful in providing high classification accuracies. In the present study we aimed at classify resting state EEGs measured from workers of a mining complex. Just after the EEG has been collected, the workers undergonetraining in a 4D virtual reality simulator that emulates the iron ore excavation from which parameters related to their performance were analyzed by the technical staff who classified the workers into four groups based on their productivity. Twoconvolutional neural networks (ConvNets) were then used to classify the workers EEG bases on the same productivity label provided by the technical staff. The neural data was used in three configurations in order to evaluate the amount of datarequired for a high accuracy classification. Isolated, the channel T5 achieved 83% of accuracy, the subtraction of channels P3 and Pz achieved 99% and using all channels simultaneously was 99.40% assertive. This study provides results that add to the recent literature showing that even simple DL architectures are able to handle complex time series such as the EEG. In addition, it pin points an application in industry with vast possibilities of expansion.

scholarly journals A Novel Convolutional Neural Network Classification Approach of Motor-Imagery EEG Recording Based on Deep Learning

2021 ◽  
Vol 11 (21) ◽  
pp. 9948
Author(s):  
Amira Echtioui ◽  
Ayoub Mlaouah ◽  
Wassim Zouch ◽  
Mohamed Ghorbel ◽  
Chokri Mhiri ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Motor Imagery ◽  
Signal To Noise Ratio ◽  
Eeg Signals ◽  
Learning Techniques ◽  
Eeg Data ◽  
Spatial And Temporal Characteristics

Recently, Electroencephalography (EEG) motor imagery (MI) signals have received increasing attention because it became possible to use these signals to encode a person’s intention to perform an action. Researchers have used MI signals to help people with partial or total paralysis, control devices such as exoskeletons, wheelchairs, prostheses, and even independent driving. Therefore, classifying the motor imagery tasks of these signals is important for a Brain-Computer Interface (BCI) system. Classifying the MI tasks from EEG signals is difficult to offer a good decoder due to the dynamic nature of the signal, its low signal-to-noise ratio, complexity, and dependence on the sensor positions. In this paper, we investigate five multilayer methods for classifying MI tasks: proposed methods based on Artificial Neural Network, Convolutional Neural Network 1 (CNN1), CNN2, CNN1 with CNN2 merged, and the modified CNN1 with CNN2 merged. These proposed methods use different spatial and temporal characteristics extracted from raw EEG data. We demonstrate that our proposed CNN1-based method outperforms state-of-the-art machine/deep learning techniques for EEG classification by an accuracy value of 68.77% and use spatial and frequency characteristics on the BCI Competition IV-2a dataset, which includes nine subjects performing four MI tasks (left/right hand, feet, and tongue). The experimental results demonstrate the feasibility of this proposed method for the classification of MI-EEG signals and can be applied successfully to BCI systems where the amount of data is large due to daily recording.

scholarly journals EEG Emotion Classification Using an Improved SincNet-Based Deep Learning Model

2019 ◽  
Vol 9 (11) ◽  
pp. 326 ◽  
Author(s):  
Hong Zeng ◽  
Zhenhua Wu ◽  
Jiaming Zhang ◽  
Chen Yang ◽  
Hua Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Speaker Recognition ◽  
Classification Accuracy ◽  
Deep Neural Network ◽  
Signal To Noise Ratio ◽  
Single Subject ◽  
Emotion Classification ◽  
Eeg Signals ◽  
Electroencephalogram Eeg ◽  
Deep Learning Model

Deep learning (DL) methods have been used increasingly widely, such as in the fields of speech and image recognition. However, how to design an appropriate DL model to accurately and efficiently classify electroencephalogram (EEG) signals is still a challenge, mainly because EEG signals are characterized by significant differences between two different subjects or vary over time within a single subject, non-stability, strong randomness, low signal-to-noise ratio. SincNet is an efficient classifier for speaker recognition, but it has some drawbacks in dealing with EEG signals classification. In this paper, we improve and propose a SincNet-based classifier, SincNet-R, which consists of three convolutional layers, and three deep neural network (DNN) layers. We then make use of SincNet-R to test the classification accuracy and robustness by emotional EEG signals. The comparable results with original SincNet model and other traditional classifiers such as CNN, LSTM and SVM, show that our proposed SincNet-R model has higher classification accuracy and better algorithm robustness.

Automatic classification of EEG signals via deep learning

2021 ◽  
Author(s):  
Tao Wu ◽  
Xiangzeng Kong ◽  
Yiwen Wang ◽  
Xue Yang ◽  
Jingxuan Liu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Automatic Classification ◽  
Eeg Signals

scholarly journals Classification of Alcoholic EEG Signals Using a Deep Learning Method

2020 ◽  
pp. 1-1
Author(s):  
Leila Farsi ◽  
Siuly Siuly ◽  
Enamul Kabir ◽  
Hua Wang
Keyword(s):  
Deep Learning ◽  
Learning Method ◽  
Eeg Signals

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>

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