Hybrid deep-learning model to recognise emotional responses of users towards architectural design alternatives

Chang, Sun-Woo; Jun, Hanjong

doi:10.1080/13467581.2019.1660663

Cited by 18 publications

(8 citation statements)

References 18 publications

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“…In the noise addition method, Gaussian noise with standard deviation of 0.001 and zero mean is applied [20]. In GAN method, the network structure is consistent with the literature [56], which is also reported in [55].…”

Section: Comparison With Data Augmentation Methodssupporting

confidence: 73%

Multi-class motor imagery EEG classification method with high accuracy and low individual differences based on hybrid neural network

Liu

Xiong

2021

J. Neural Eng.

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Objective. Most current methods of classifying different patterns for motor imagery EEG signals require complex pre-processing and feature extraction steps, which consume time and lack adaptability, ignoring individual differences in EEG signals. It is essential to improve algorithm performance with the increased classes and diversity of subjects. Approach. This study introduces deep learning method for end-to-end learning to complete the classification of four-class MI tasks, aiming to improve the recognition rate and balance the classification accuracy among different subjects. A new one-dimensional input data representation method is proposed. This representation method can increase the number of samples and ignore the influence of channel correlation. In addition, a cascade network of convolutional neural network and gated recurrent unit is designed to learn time-frequency information from EEG data without extracting features manually, this model can capture the hidden representations related to different MI mode of each people. Main results. Experiments on BCI Competition 2a dataset and actual collected dataset achieve high accuracy near 99.40% and 92.56%, and the standard deviation is 0.34 and 1.35 respectively. Results demonstrate that the proposed method outperforms the advanced methods and baseline models. Significance. Experimental results show that the proposed method improves the accuracy of multi-classification and overcomes the impact of individual differences on classification by training neural network subject-dependent, which promotes the development of actual brain-computer interface systems.

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Section: Comparison With Data Augmentation Methodssupporting

confidence: 73%

Multi-class motor imagery EEG classification method with high accuracy and low individual differences based on hybrid neural network

Liu

Xiong

2021

J. Neural Eng.

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“…Chang et al (2019) used GAN to increase the size of dataset for a 2-class emotion recognition task [74]. The generator and discriminator of the GAN consists of three hidden layers, which consists of 50, 100, and 50 nodes, respectively.…”

Section: Noise Additionmentioning

confidence: 99%

Data augmentation for deep-learning-based electroencephalography

Lashgari

Liang

Maoz

2020

Journal of Neuroscience Methods

237

140

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Highlights • Data augmentation (DA) is increasingly used with deep learning (DL) on EEG • It enhances decoding accuracy left unexplained by 29% on average on the datasets we review • We analyze which specific DA techniques appear to work best for which EEG tasks • We tested various DA techniques on an open motor-imagery task and compared the accuracy gains to demonstrate the usefulness of DA for DL-based EEG analysis • We propose guidelines for reporting parameters for different DA techniques Abstract-Background Data augmentation (DA) has recently been demonstrated to achieve considerable performance gains for deep learning (DL)-increased accuracy and stability and reduced overfitting. Some electroencephalography (EEG) tasks suffer from low samples-to-features ratio, severely reducing DL effectiveness. DA with DL thus holds transformative promise for EEG processing, possibly like DL revolutionized computer vision, etc.-New method We review trends and approaches to DA for DL in EEG to address: Which DA approaches exist and are common for which EEG tasks? What input features are used? And, what kind of accuracy gain can be expected?-Results DA for DL on EEG begun 5 years ago and is steadily used more. We grouped DA techniques (noise addition, generative adversarial networks, sliding windows, sampling, Fourier transform, recombination of segmentation, and others) and EEG tasks (into seizure detection, sleep stages, motor imagery, mental workload, emotion recognition, motor tasks, and visual tasks). DA efficacy across techniques varied considerably. Noise addition and sliding windows provided the highest accuracy boost; mental workload most benefitted from DA. Sliding window, noise addition, and sampling methods most common for seizure detection, mental workload, and sleep stages, respectively.-Comparing with existing methods Percent of decoding accuracy explained by DA beyond unaugmented accuracy varied between 8% for recombination of segmentation and 36% for noise addition and from 14% for motor imagery to 56% for mental workload-29% on average.-Conclusions DA increasingly used and considerably improved DL decoding accuracy on EEG. Additional publications-if adhering to our reporting guidelines-will facilitate more detailed analysis.

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“…Data augmentation approaches such as overlapping or sliding window [37]- [40] and generative adversarial networks (GAN) [41]- [43] generate more training data from the existing data [44]. GAN is not the ideal approach for the current problem since the amount of available data is very limited.…”

Section: Methods Used To Augment Datamentioning

confidence: 99%

Decoding Imagined Speech From EEG Using Transfer Learning

Panachakel

Ganesan

2021

IEEE Access

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We present a transfer learning based approach for decoding imagined speech from electroencephalogram (EEG). Features are extracted simultaneously from multiple EEG channels, rather than separately from individual channels. This helps in capturing the interrelationships between the cortical regions. To alleviate the problem of lack of enough data for training deep networks, sliding window based data augmentation is performed. Mean phase coherence and magnitude-squared coherence, two popular measures used in EEG connectivity analysis, are used as features. These features are compactly arranged, exploiting their symmetry, to obtain a three dimensional "image-like" representation. The three dimensions of this matrix correspond to the alpha, beta and gamma EEG frequency bands. A deep network with ResNet50 as the base model is used for classifying the imagined prompts. The proposed method is tested on the publicly available ASU dataset of imagined speech EEG, comprising four different types of prompts. The accuracy of decoding the imagined prompt varies from a minimum of 79.7% for vowels to a maximum of 95.5% for short-long words across the various subjects. The accuracies obtained are better than the stateof-the-art methods, and the technique is good in decoding prompts of different complexities.

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Hybrid deep-learning model to recognise emotional responses of users towards architectural design alternatives

Cited by 18 publications

References 18 publications

Multi-class motor imagery EEG classification method with high accuracy and low individual differences based on hybrid neural network

Multi-class motor imagery EEG classification method with high accuracy and low individual differences based on hybrid neural network

Data augmentation for deep-learning-based electroencephalography

Decoding Imagined Speech From EEG Using Transfer Learning

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