scholarly journals Multi-Channel Convolutional Neural Networks Architecture Feeding for Effective EEG Mental Tasks Classification

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3451 ◽  
Author(s):  
Sławomir Opałka ◽  
Bartłomiej Stasiak ◽  
Dominik Szajerman ◽  
Adam Wojciechowski
Keyword(s):  
Neural Network ◽  
Frequency Domain ◽  
Classification Accuracy ◽  
State Of The Art ◽  
Parameters Optimization ◽  
Alternative Methods ◽  
Signal Frequency ◽  
Eeg Signal ◽  
Mental Tasks Classification ◽  
Mental Tasks

Mental tasks classification is increasingly recognized as a major challenge in the field of EEG signal processing and analysis. State-of-the-art approaches face the issue of spatially unstable structure of highly noised EEG signals. To address this problem, this paper presents a multi-channel convolutional neural network architecture with adaptively optimized parameters. Our solution outperforms alternative methods in terms of classification accuracy of mental tasks (imagination of hand movements and speech sounds generation) while providing high generalization capability (∼5%). Classification efficiency was obtained by using a frequency-domain multi-channel neural network feeding scheme by EEG signal frequency sub-bands analysis and architecture supporting feature mapping with two subsequent convolutional layers terminated with a fully connected layer. For dataset V from BCI Competition III, the method achieved an average classification accuracy level of nearly 70%, outperforming alternative methods. The solution presented applies a frequency domain for input data processed by a multi-channel architecture that isolates frequency sub-bands in time windows, which enables multi-class signal classification that is highly generalizable and more accurate (∼1.2%) than the existing solutions. Such an approach, combined with an appropriate learning strategy and parameters optimization, adapted to signal characteristics, outperforms reference single- or multi-channel networks, such as AlexNet, VGG-16 and Cecotti’s multi-channel NN. With the classification accuracy improvement of 1.2%, our solution is a clear advance as compared to the top three state-of-the-art methods, which achieved the result of no more than 0.3%.

scholarly journals Classification of Emotional States Inparkinson’s Disease Patients Using Time,Frequency and Time-Frequency Analysis

2021 ◽  
Author(s):  
Rejith K.N ◽  
Kamalraj Subramaniam ◽  
Ayyem Pillai Vasudevan Pillai ◽  
Roshini T V ◽  
Renjith V. Ravi ◽  
...  
Keyword(s):  
Frequency Domain ◽  
Classification Accuracy ◽  
Machine Learning Algorithms ◽  
Signal Frequency ◽  
Spectral Energy ◽  
Support Vector ◽  
Svm Classifier ◽  
Time Frequency ◽  
Energy Entropy ◽  
Teager Energy

Abstract In this work, PD patients and healthy individuals were categorized with machine-learning algorithms. EEG signals associated with six different emotions, (Happiness(E1), Sadness(E2), Fear(E3), Anger(E4), Surprise,(E5) and disgust(E6)) were used for the study. EEG data were collected from 20 PD patients and 20 normal controls using multimodal stimuli. Different features were used to categorize emotional data. Emotional recognition in Parkinson’s disease (PD) has been investigated in three domains namely, time, frequency and time frequency using Entropy, Energy-Entropy and Teager Energy-Entropy features. Three classifiers namely, K-Nearest Neighbor Algorithm, Support Vector Machine and Probabilistic Neural Network were used to observethe classification results. Emotional EEG stimuli such as anger, surprise, happiness, sadness, fear, and disgust were used to categorize PD patients and healthy controls (HC). For each EEG signal, frequency features corresponding to alpha, beta and gamma bands were obtained for nine feature extraction methods (Entropy, Energy Entropy, Teager Energy Entropy, Spectral Entropy, Spectral Energy-Entropy, Spectral Teager Energy-Entropy, STFT Entropy, STFT Energy-Entropy and STFT Teager Energy-Entropy). From the analysis, it is observed that the entropy feature in frequency domain performs evenly well (above 80 %) for all six emotions with KNN. Classification results shows that using the selected energy entropy combination feature in frequency domain provides highest accuracy for all emotions except E1 and E2 for KNN and SVM classifier, whereas other features give accuracy values of above 60% for most emotions.It is also observed that emotion E1 gives above 90 % classification accuracy for all classifiers in time domain.In frequency domain also, emotion E1 gives above 90% classification accuracy using PNN classifier.

scholarly journals DEEP LEARNING BASED SKIN LESIONS DIAGNOSIS

2019 ◽  
Vol XLII-2/W12 ◽  
pp. 81-85 ◽  
Author(s):  
D. A. Gavrilov ◽  
N. N. Shchelkunov ◽  
A. V. Melerzanov
Keyword(s):  
Neural Network ◽  
Classification Accuracy ◽  
Intelligent Systems ◽  
State Of The Art ◽  
Skin Lesions ◽  
Deep Convolutional Neural Networks ◽  
Melanoma Diagnosis ◽  
Malignant Skin ◽  
Diagnosis Accuracy ◽  
Visual Diagnosis

<p><strong>Abstract.</strong> Melanoma is one of the most virulent lesions of human’s skin. The visual diagnosis accuracy of melanoma directly depends on the doctor’s qualification and specialization. State-of-the-art solutions in the field of image processing and machine learning allows to create intelligent systems based on artificial convolutional neural network exceeding human’s rates in the field of object classification, including the case of malignant skin lesions. This paper presents an algorithm for the early melanoma diagnosis based on artificial deep convolutional neural networks. The algorithm proposed allows to reach the classification accuracy of melanoma at least 91%.</p>

scholarly journals Compressed Wavelet Tensor Attention Capsule Network

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Xiushan Liu ◽  
Chun Shan ◽  
Qin Zhang ◽  
Jun Cheng ◽  
Peng Xu
Keyword(s):  
Neural Network ◽  
Frequency Domain ◽  
Wavelet Decomposition ◽  
State Of The Art ◽  
Texture Classification ◽  
Texture Features ◽  
Edge Computing ◽  
Spectral Features ◽  
Classification Methods ◽  
Classification Tasks

Texture classification plays an important role for various computer vision tasks. Depending upon the powerful feature extraction capability, convolutional neural network (CNN)-based texture classification methods have attracted extensive attention. However, there still exist many challenges, such as the extraction of multilevel texture features and the exploration of multidirectional relationships. To address the problem, this paper proposes the compressed wavelet tensor attention capsule network (CWTACapsNet), which integrates multiscale wavelet decomposition, tensor attention blocks, and quantization techniques into the framework of capsule neural network. Specifically, the multilevel wavelet decomposition is in charge of extracting multiscale spectral features in frequency domain; in addition, the tensor attention blocks explore the multidimensional dependencies of convolutional feature channels, and the quantization techniques make the computational storage complexities be suitable for edge computing requirements. The proposed CWTACapsNet provides an efficient way to explore spatial domain features, frequency domain features, and their dependencies which are useful for most texture classification tasks. Furthermore, CWTACapsNet benefits from quantization techniques and is suitable for edge computing applications. Experimental results on several texture datasets show that the proposed CWTACapsNet outperforms the state-of-the-art texture classification methods not only in accuracy but also in robustness.

scholarly journals Weighted Random Search for CNN Hyperparameter Optimization

2020 ◽  
Vol 15 (2) ◽  
Author(s):  
Razvan Andonie ◽  
Adrian-Catalin Florea
Keyword(s):  
Neural Network ◽  
Classification Accuracy ◽  
State Of The Art ◽  
Random Search ◽  
Optimization Methods ◽  
Optimal Combination ◽  
The Other ◽  
Greedy Heuristic ◽  
Training Phase ◽  
Hyperparameter Optimization

Nearly all model algorithms used in machine learning use two different sets of parameters: the training parameters and the meta-parameters (hyperparameters). While the training parameters are learned during the training phase, the values of the hyperparameters have to be specified before learning starts. For a given dataset, we would like to find the optimal combination of hyperparameter values, in a reasonable amount of time. This is a challenging task because of its computational complexity. In previous work [11], we introduced the Weighted Random Search (WRS) method, a combination of Random Search (RS) and probabilistic greedy heuristic. In the current paper, we compare the WRS method with several state-of-the art hyperparameter optimization methods with respect to Convolutional Neural Network (CNN) hyperparameter optimization. The criterion is the classification accuracy achieved within the same number of tested combinations of hyperparameter values. According to our experiments, the WRS algorithm outperforms the other methods.

scholarly journals A Novel Time-Incremental End-to-End Shared Neural Network with Attention-Based Feature Fusion for Multiclass Motor Imagery Recognition

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Shidong Lian ◽  
Jialin Xu ◽  
Guokun Zuo ◽  
Xia Wei ◽  
Huilin Zhou
Keyword(s):  
Neural Network ◽  
Motor Imagery ◽  
Classification Accuracy ◽  
Feature Fusion ◽  
Spatial Domain ◽  
Convolution Neural Network ◽  
Eeg Signal ◽  
Signal Recognition ◽  
Eeg Signals ◽  
End To End

In the research of motor imagery brain-computer interface (MI-BCI), traditional electroencephalogram (EEG) signal recognition algorithms appear to be inefficient in extracting EEG signal features and improving classification accuracy. In this paper, we discuss a solution to this problem based on a novel step-by-step method of feature extraction and pattern classification for multiclass MI-EEG signals. First, the training data from all subjects is merged and enlarged through autoencoder to meet the need for massive amounts of data while reducing the bad effect on signal recognition because of randomness, instability, and individual variability of EEG data. Second, an end-to-end sharing structure with attention-based time-incremental shallow convolution neural network is proposed. Shallow convolution neural network (SCNN) and bidirectional long short-term memory (BiLSTM) network are used to extract frequency-spatial domain features and time-series features of EEG signals, respectively. Then, the attention model is introduced into the feature fusion layer to dynamically weight these extracted temporal-frequency-spatial domain features, which greatly contributes to the reduction of feature redundancy and the improvement of classification accuracy. At last, validation tests using BCI Competition IV 2a data sets show that classification accuracy and kappa coefficient have reached 82.7 ± 5.57% and 0.78 ± 0.074, which can strongly prove its advantages in improving classification accuracy and reducing individual difference among different subjects from the same network.

LSTM multichannel neural networks in mental task classification

2019 ◽  
Vol 38 (4) ◽  
pp. 1204-1213
Author(s):  
Sławomir Opałka ◽  
Dominik Szajerman ◽  
Adam Wojciechowski
Keyword(s):  
Neural Networks ◽  
Short Term Memory ◽  
State Of The Art ◽  
Mental Task ◽  
Data Set ◽  
Content Type ◽  
Architecture Model ◽  
Task Classification ◽  
Mental Tasks Classification ◽  
Mental Tasks

Purpose The purpose of this paper is to apply recurrent neural networks (RNNs) and more specifically long-short term memory (LSTM)-based ones for mental task classification in terms of BCI systems. The authors have introduced novel LSTM-based multichannel architecture model which proved to be highly promising in other fields, yet was not used for mental tasks classification. Design/methodology/approach Validity of the multichannel LSTM-based solution was confronted with the results achieved by a non-multichannel state-of-the-art solutions on a well-recognized data set. Findings The results demonstrated evident advantage of the introduced method. The best of the provided variants outperformed most of the RNNs approaches and was comparable with the best state-of-the-art methods. Practical implications The approach presented in the manuscript enables more detailed investigation of the electroencephalography analysis methods, invaluable for BCI mental tasks classification. Originality/value The new approach to mental task classification, exploiting LSTM-based RNNs with multichannel architecture, operating on spatial features retrieving filters, has been adapted to mental tasks with noticeable results. To the best of the authors’ knowledge, such an approach was not present in the literature before.

scholarly journals Multi-scale 3D-convolutional neural network for hyperspectral image classification

2022 ◽  
Vol 25 (1) ◽  
pp. 307
Author(s):  
Murali Kanthi ◽  
Thogarcheti Hitendra Sarma ◽  
Chigarapalle Shoba Bindu
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Classification Accuracy ◽  
Hyperspectral Image ◽  
State Of The Art ◽  
Spatial Dimension ◽  
Multi Scale ◽  
Proposed Model ◽  
Spectral Channels

Deep Learning methods are state-of-the-art approaches for pixel-based hyperspectral images (HSI) classification. High classification accuracy has been achieved by extracting deep features from both spatial-spectral channels. However, the efficiency of such spatial-spectral approaches depends on the spatial dimension of each patch and there is no theoretically valid approach to find the optimum spatial dimension to be considered. It is more valid to extract spatial features by considering varying neighborhood scales in spatial dimensions. In this regard, this article proposes a deep convolutional neural network (CNN) model wherein three different multi-scale spatial-spectral patches are used to extract the features in both the spatial and spectral channels. In order to extract these potential features, the proposed deep learning architecture takes three patches various scales in spatial dimension. 3D convolution is performed on each selected patch and the process runs through entire image. The proposed is named as multi-scale three-dimensional convolutional neural network (MS-3DCNN). The efficiency of the proposed model is being verified through the experimental studies on three publicly available benchmark datasets including Pavia University, Indian Pines, and Salinas. It is empirically proved that the classification accuracy of the proposed model is improved when compared with the remaining state-of-the-art methods.

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