A Deep Neural Network Classifier for Decoding Human Brain Activity Based on Magnetoencephalography

Çalışkan, Abdullah; Yüksel, Mehmet Emin; Badem, Hasan; Baştürk, Alper

doi:10.5755/j01.eie.23.2.18002

Cited by 25 publications

(24 citation statements)

References 18 publications

(21 reference statements)

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“…In recent years, statistical machine learning methods have been introduced into the field of neuroimaging [18,19]. These methods have been increasingly used for feature extraction, classification, and decoding in MEG and EEG [20][21][22]. The different approaches can be organized into supervised, unsupervised, semisupervised, or reinforcement learning based on the desired outcome of the algorithm.…”

Section: Journal Of Engineeringmentioning

confidence: 99%

Deep Learning Approach for Automatic Classification of Ocular and Cardiac Artifacts in MEG Data

Hasasneh

Kampel

Sripad

et al. 2018

Journal of Engineering

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We propose an artifact classification scheme based on a combined deep and convolutional neural network (DCNN) model, to automatically identify cardiac and ocular artifacts from neuromagnetic data, without the need for additional electrocardiogram (ECG) and electrooculogram (EOG) recordings. From independent components, the model uses both the spatial and temporal information of the decomposed magnetoencephalography (MEG) data. In total, 7122 samples were used after data augmentation, in which task and nontask related MEG recordings from 48 subjects served as the database for this study. Artifact rejection was applied using the combined model, which achieved a sensitivity and specificity of 91.8% and 97.4%, respectively. The overall accuracy of the model was validated using a cross-validation test and revealed a median accuracy of 94.4%, indicating high reliability of the DCNN-based artifact removal in task and nontask related MEG experiments. The major advantages of the proposed method are as follows: (1) it is a fully automated and user independent workflow of artifact classification in MEG data; (2) once the model is trained there is no need for auxiliary signal recordings; (3) the flexibility in the model design and training allows for various modalities (MEG/EEG) and various sensor types.

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Section: Journal Of Engineeringmentioning

confidence: 99%

Deep Learning Approach for Automatic Classification of Ocular and Cardiac Artifacts in MEG Data

Hasasneh

Kampel

Sripad

et al. 2018

Journal of Engineering

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“…Deep learning is very useful in many machine learning tasks, including speech, image, and signal analysis as well as numerous classification problems [17,[20][21][22][23]. There are a number of DNN architectures, including convolutional neural networks, stacked autoencoders, and Boltzmann machines.…”

Section: Deep Neural Network Modelmentioning

confidence: 99%

Prediction of Leakage from an Axial Piston Pump Slipper with Circular Dimples Using Deep Neural Networks

Özmen

Sınanoğlu

Çalışkan

et al. 2020

Chin. J. Mech. Eng.

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Oil leakage between the slipper and swash plate of an axial piston pump has a significant effect on the efficiency of the pump. Therefore, it is extremely important that any leakage can be predicted. This study investigates the leakage, oil film thickness, and pocket pressure values of a slipper with circular dimples under different working conditions. The results reveal that flat slippers suffer less leakage than those with textured surfaces. Also, a deep learning-based framework is proposed for modeling the slipper behavior. This framework is a long short-term memory-based deep neural network, which has been extremely successful in predicting time series. The model is compared with four conventional machine learning methods. In addition, statistical analyses and comparisons confirm the superiority of the proposed model.

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“…DNN is superior to other conventional neural networks in the classification problem with the help of the aforementioned properties by having complex decision surface. 6,7,[22][23][24][25][26][27][28][29]…”

Section: Introductionmentioning

confidence: 99%

Type 2 diabetes data classification using stacked autoencoders in deep neural networks

Kannadasan

Edla

Kuppili

2019

Clinical Epidemiology and Global Health

118

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This paper aims to classify the Pima Indians diabetes dataset with better accuracy and other evaluation metrics. The Deep Neural Network (DNN) framework will help to diagnose the patient in an effective way with higher accuracy. Method: In this approach, we proposed a Deep Neural Network framework for diabetes data classification using stacked autoencoders. Features are extracted from the dataset using stacked autoencoders and the dataset is classified using softmax layer. Also, fine tuning of the network is done using backpropagation in supervised fashion with the training dataset. However, the medical diagnosis involves the risk factors of wrong prediction; hence we have used evaluation metrics such as precision, recall, specificity and F1-score for the evaluation of our model and have achieved better results. Results: The proposed framework is experimented on Pima Indians Diabetes data which has 768 patient records with 8 attributes for each record. We achieved classification accuracy of 86.26%. Conclusion: A stacked autoencoders based Deep Learning framework for classification of Type 2 Diabetes data is proposed in this paper. This approach is experimented on UCI machine learning data and proved the outperformance over various existing classification methods.

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A Deep Neural Network Classifier for Decoding Human Brain Activity Based on Magnetoencephalography

Cited by 25 publications

References 18 publications

Deep Learning Approach for Automatic Classification of Ocular and Cardiac Artifacts in MEG Data

Deep Learning Approach for Automatic Classification of Ocular and Cardiac Artifacts in MEG Data

Prediction of Leakage from an Axial Piston Pump Slipper with Circular Dimples Using Deep Neural Networks

Type 2 diabetes data classification using stacked autoencoders in deep neural networks

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