An Approach for EEG Denoising Based on Wasserstein Generative Adversarial Network

Dong, Yuanzhe; Tang, Xi; Li, Qingge; Wang, Yingying; Jiang, Naifu; Tian, Lan; Zheng, Yue; Li, Xiangxin; Zhao, Shaofeng; Li, Guanglin; Fang, Peng

doi:10.1109/tnsre.2023.3309815

Cited by 4 publications

(3 citation statements)

References 52 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…During this process, EEG data is visually inspected to remove segments with significant movement, thus obtaining pure EEG signals [35]. Visual inspection is also performed to excise artifact-affected data segments [35]. To further improve quality, a 50 Hz notch filter targets power line noise [36].…”

Section: As X = (16)mentioning

confidence: 99%

See 1 more Smart Citation

Temporal Convolutional Network-Enhanced Real-Time Implicit Emotion Recognition with an Innovative Wearable fNIRS-EEG Dual-Modal System

Chen,

Yu,

Wang

et al. 2024

Electronics

View full text Add to dashboard Cite

Emotion recognition remains an intricate task at the crossroads of psychology and artificial intelligence, necessitating real-time, accurate discernment of implicit emotional states. Here, we introduce a pioneering wearable dual-modal device, synergizing functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) to meet this demand. The first-of-its-kind fNIRS-EEG ensemble exploits a temporal convolutional network (TC-ResNet) that takes 24 fNIRS and 16 EEG channels as input for the extraction and recognition of emotional features. Our system has many advantages including its portability, battery efficiency, wireless capabilities, and scalable architecture. It offers a real-time visual interface for the observation of cerebral electrical and hemodynamic changes, tailored for a variety of real-world scenarios. Our approach is a comprehensive emotional detection strategy, with new designs in system architecture and deployment and improvement in signal processing and interpretation. We examine the interplay of emotions and physiological responses to elucidate the cognitive processes of emotion regulation. An extensive evaluation of 30 subjects under four emotion induction protocols demonstrates our bimodal system’s excellence in detecting emotions, with an impressive classification accuracy of 99.81% and its ability to reveal the interconnection between fNIRS and EEG signals. Compared with the latest unimodal identification methods, our bimodal approach shows significant accuracy gains of 0.24% for EEG and 8.37% for fNIRS. Moreover, our proposed TC-ResNet-driven temporal convolutional fusion technique outperforms conventional EEG-fNIRS fusion methods, improving the recognition accuracy from 0.7% to 32.98%. This research presents a groundbreaking advancement in affective computing that combines biological engineering and artificial intelligence. Our integrated solution facilitates nuanced and responsive affective intelligence in practical applications, with far-reaching impacts on personalized healthcare, education, and human–computer interaction paradigms.

show abstract

Section: As X = (16)mentioning

confidence: 99%

“…During this process, EEG data is visually inspected to remove segments with significant movement, thus obtaining pure EEG signals [35].…”

Section: As X = (16)mentioning

confidence: 99%

Temporal Convolutional Network-Enhanced Real-Time Implicit Emotion Recognition with an Innovative Wearable fNIRS-EEG Dual-Modal System

Chen,

Yu,

Wang

et al. 2024

Electronics

View full text Add to dashboard Cite

show abstract

“…Fifth, the classifier performance is heavily dependent on the EEG signal denoising effect, and the commonly used ICA method for preprocessing is used in this paper. Some more robust preprocessing denoising methods have been proposed in recent years [49][50][51][52][53] which may further enhance the classification effect. We will further evaluate the effect of EEG noise on classification performance in future studies.…”

Section: Discussionmentioning

confidence: 99%

Decoding Subject-Driven Cognitive States from EEG Signals for Cognitive Brain–Computer Interface

Huang,

Wang,

Fan

et al. 2024

Brain Sciences

View full text Add to dashboard Cite

In this study, we investigated the feasibility of using electroencephalogram (EEG) signals to differentiate between four distinct subject-driven cognitive states: resting state, narrative memory, music, and subtraction tasks. EEG data were collected from seven healthy male participants while performing these cognitive tasks, and the raw EEG signals were transformed into time–frequency maps using continuous wavelet transform. Based on these time–frequency maps, we developed a convolutional neural network model (TF-CNN-CFA) with a channel and frequency attention mechanism to automatically distinguish between these cognitive states. The experimental results demonstrated that the model achieved an average classification accuracy of 76.14% in identifying these four cognitive states, significantly outperforming traditional EEG signal processing methods and other classical image classification algorithms. Furthermore, we investigated the impact of varying lengths of EEG signals on classification performance and found that TF-CNN-CFA demonstrates consistent performance across different window lengths, indicating its strong generalization capability. This study validates the ability of EEG to differentiate higher cognitive states, which could potentially offer a novel BCI paradigm.

show abstract

GAN-ACNN: a design decision-making algorithm based on EEG signals from different brain regions

Yang,

Xue,

et al. 2024

Eng. Res. Express

View full text Add to dashboard Cite

Decision-making is an integral part of an individual's life. Due to the small amount of data in the art design decision dataset, we increase the amount of data using data augmentation. However, different brain regions have distinct effects on the accuracy of art design decisions, so we divide the brain into four areas and investigate the influence of each on the results. We collect electroencephalogram (EEG) data from 16 subjects, apply a band-pass filter to filter it, and then feed it into a generative adversarial network (GAN) for data augmentation. The augmented EEG data is input to a convolutional neural network with a self-attention mechanism (ACNN). The experimental results show that the degree of influence of different brain regions is: right frontal lobe > right parietal-temporal-occipital lobe > left frontal lobe > left parietal-temporal-occipital lobe. In view of this, we find the six optimal channels for art design decisions, and their prediction results are better than those of all channels. In addition, our GAN-ACNN model achieves an average accuracy of 93.51%, which is an effective method. Finally, we also classify the DEAP dataset to evaluate the robustness of the model.

show abstract

An Approach for EEG Denoising Based on Wasserstein Generative Adversarial Network

Cited by 4 publications

References 52 publications

Temporal Convolutional Network-Enhanced Real-Time Implicit Emotion Recognition with an Innovative Wearable fNIRS-EEG Dual-Modal System

Temporal Convolutional Network-Enhanced Real-Time Implicit Emotion Recognition with an Innovative Wearable fNIRS-EEG Dual-Modal System

Decoding Subject-Driven Cognitive States from EEG Signals for Cognitive Brain–Computer Interface

GAN-ACNN: a design decision-making algorithm based on EEG signals from different brain regions

Contact Info

Product

Resources

About