Deep Learning with ConvNet Predicts Imagery Tasks Through EEG

Altan, Gökhan; Yayık, Apdullah; Kutlu, Yakup

doi:10.1007/s11063-021-10533-7

Cited by 25 publications

(11 citation statements)

References 40 publications

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“…It also performs better on comparison with, from where the dataset (D2) is borrowed which is used in the 4-class classification tasks. The model showed an improvement of 0.69% for 4-class classification task and 2.13% in overall accuracy for 3 class classification task on the dataset D2 [60] over the method proposed by Khan et al [23] . It was observed that the proposed method showed the quite significant improvements in accuracy, precision, recall for COVID-19 class (as seen in Table 3 ), which is paramount for this study as miss-classification of a COVID-19 case can have serious repercussions than other classes.…”

Section: Comparative Analysis Of State-of-the-art Deep Learning Methodsmentioning

confidence: 74%

“…The model showed an improvement of 0.69% for 4-class classification task and 2.13% in overall accuracy for 3 class classification task on the dataset D2 [60] over the method proposed by Khan et al [23] .…”

Section: Comparative Analysis Of State-of-the-art Deep Learning Methodsmentioning

confidence: 74%

“…The performance for each class shows improvements and COVID-19 class shows an average precision value of 0.9791 and recall 0.9591 for the fusion model over 4-folds. The performance of the proposed method was also validated on the dataset [60] with slight adjustments to fit our purposes. D1 has some different sources and formatting and count from the one provided in D2.…”

Section: Comparative Analysis Of State-of-the-art Deep Learning Methodsmentioning

confidence: 99%

“…Later, Chouhan et al [14] suggested a transfer learning-based approach for the same. The recent progress of deep learning applications in medical image analysis has proven to be an effective tool for diagnosis of various diseases like breast cancer and many other pulmonary disorders [59] , [60] , [61] . Y-D. Zhang et al [62] describe MIDCAN, A multiple input deep convolutional attention network that can handle CCT and CXR images simultaneously.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A novel fusion based convolutional neural network approach for classification of COVID-19 from chest X-ray images

Sharma

Singh

Koundal

2022

Biomedical Signal Processing and Control

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Section: Comparative Analysis Of State-of-the-art Deep Learning Methodsmentioning

confidence: 74%

Section: Comparative Analysis Of State-of-the-art Deep Learning Methodsmentioning

confidence: 74%

Section: Comparative Analysis Of State-of-the-art Deep Learning Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A novel fusion based convolutional neural network approach for classification of COVID-19 from chest X-ray images

Sharma

Singh

Koundal

2022

Biomedical Signal Processing and Control

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“…The evaluation models used are also different for different data types. Common models are mainly based on machine learning [ 14 – 16 ] and deep learning [ 17 – 19 ]. Because the physiology is more realistic, this study mainly chooses the data based on the physiological signal to identify the stress state.…”

Section: Introductionmentioning

confidence: 99%

A Novel Stress State Assessment Method for College Students Based on EEG

Liu

Gao

et al. 2022

Computational Intelligence and Neuroscience

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Stress is an unavoidable problem for today’s college students. Stress can arouse strong personal emotional and behavioral responses. Compared with other groups of the same age, college students have a special way of life and living environment. They have complex interpersonal relationships and relatively weak social support systems. At the same time, they also face fierce competition in both academic and employment. However, they lack the skills to deal with the crisis and are reluctant to ask others for help, which leads to a simultaneous increase in mental stress. The pressure on college students mainly comes from study, family, social, employment, society, and economy. When students face multiple pressures from family, school, society, etc., some students are prone to some psychological problems due to their own personality or external environment and other reasons. Therefore, regular assessment of students’ stress status is an important means to prevent college students’ psychological problems. Considering that in real life, the number of students whose pressure is within the tolerable range is the majority, while the number of students who are under too much pressure is a minority. Therefore, the actual dataset to be identified belongs to a kind of imbalanced data. In this study, an improved extreme learning machine (IELM) is used to improve the performance of the recognition model as much as possible. IELM takes the idea of label weighting as the starting point, introduces the AdaBoost algorithm, and combines its weight distribution with the label weighted extreme learning machine (ELM). During the weight update process, the advantage of the imbalanced nature of multi-label datasets is taken. IELM was used to classify EEG data to determine the stress level of college students. The experimental results demonstrate that the algorithm used in this study has excellent classification performance and can accurately assess students’ stress levels. The accurate assessment of stress has provided a solid foundation for the development of students’ mental health and has significant practical implications.

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General and patient-specific seizure classification using deep neural networks

Massoud

Abdelzaher

Kuhlmann

et al. 2023

Analog Integr Circ Sig Process

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Seizure prediction algorithms have been central in the field of data analysis for the improvement of epileptic patients’ lives. The most recent advancements of which include the use of deep neural networks to present an optimized, accurate seizure prediction system. This work puts forth deep learning methods to automate the process of epileptic seizure detection with electroencephalogram (EEG) signals as input; both a patient-specific and general approach are followed. EEG signals are time structure series motivating the use of sequence algorithms such as temporal convolutional neural networks (TCNNs), and long short-term memory networks. We then compare this methodology to other prior pre-implemented structures, including our previous work for seizure prediction using machine learning approaches support vector machine and random under-sampling boost. Moreover, patient-specific and general seizure prediction approaches are used to evaluate the performance of the best algorithms. Area under curve (AUC) is used to select the best performing algorithm to account for the imbalanced dataset. The presented TCNN model showed the best patient-specific results than that of the general approach with, AUC of 0.73, while ML model had the best results for general classification with AUC of 0.75.

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Deep Learning with ConvNet Predicts Imagery Tasks Through EEG

Cited by 25 publications

References 40 publications

A novel fusion based convolutional neural network approach for classification of COVID-19 from chest X-ray images

A novel fusion based convolutional neural network approach for classification of COVID-19 from chest X-ray images

A Novel Stress State Assessment Method for College Students Based on EEG

General and patient-specific seizure classification using deep neural networks

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