Ultra Efficient Transfer Learning with Meta Update for Cross Subject EEG Classification

Duan, Tiehang; Chauhan, Mihir; Shaikh, Mohammad Abuzar; Chu, Jun; Srihari, Sargur N.

doi:10.48550/arxiv.2003.06113

Cited by 6 publications

(8 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Secondly, our own recorded MI dataset consisting of nine subject is further utilized for investigation. Lastly, an MI dataset acquired from a publicly available database, known as BCI competition IV-a, is utilized to validate the classification performance when the EMC-MMFT framework is employed [23]. Moreover, all the datasets are also utilized for a comparative performance analysis when ROD, DTE, and LSA-MMFT are applied.…”

Section: Dataset Descriptionsmentioning

confidence: 99%

Comparison of Domain Selection Methods for Multi-Source Manifold Feature Transfer Learning in Electroencephalogram Classification

Maswanganyi,

Tu,

Owolawi

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

Transfer learning (TL) utilizes knowledge from the source domain (SD) to enhance the classification rate in the target domain (TD). It has been widely used to address the challenge of sessional and inter-subject variations in electroencephalogram (EEG)-based brain–computer interfaces (BCIs). However, utilizing knowledge from a combination of both related and non-related sources can significantly deteriorate the classification performance across individual target domains, resulting in a negative transfer (NT). Hence, NT becomes one of the most significant challenges for transfer learning algorithms. Notably, domain selection techniques have been developed to address the challenge of NT emerging from the transfer of knowledge from non-related sources. However, existing domain selection approaches iterate through domains and remove a single low-beneficial domain at a time, which can massively affect the classification performance in each iteration since SDs respond differently to other sources. In this paper, we compare domain selection techniques for a multi-source manifold feature transfer learning (MMFT) framework to address the challenge of NT and then evaluate the effect of beneficial and non-beneficial sources on TL performance. To evaluate the effect of low-beneficial and high beneficial sources on TL performance, some commonly used domain selection methods are compared, namely, domain transferability estimation (DTE), rank of domain (ROD), label similarity analysis, and enhanced multi-class MMFT (EMC-MMFT), using the same multi-class cross-session and cross-subject classification problems. The experimental results demonstrate the superiority of the EMC-MMFT algorithm in terms of minimizing the effect of NT. The highest classification accuracy (CA) of 100% is achieved when optimal combinations of high beneficial sources are selected for two-class SSMVEP problems, while the highest CAs of 98% and 87% are achieved for three- and four-class SSMVEP problems, respectively. The highest CA of 98% is achieved for two-class MI classification problems, while the highest CAs of 90% and 71.5% are obtained for three- and four-class MI problems, respectively.

show abstract

Section: Dataset Descriptionsmentioning

confidence: 99%

Comparison of Domain Selection Methods for Multi-Source Manifold Feature Transfer Learning in Electroencephalogram Classification

Maswanganyi,

Tu,

Owolawi

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…In addition to DAN approaches, meta-learning techniques such as MUPS-EEG [203] and MAML [204] have accelerated the transfer process and improved adaptation to new subjects. Also, the Standardization-Refinement Domain Adaptation (SRDA) method utilizes adaptive batch normalization and a loss function based on the variation in information to increase the similarity of marginal distributions between source and target domains [218].…”

Section: Machine Learning Approachmentioning

confidence: 99%

Review of EEG Affective Recognition with a Neuroscience Perspective

Lim,

Lew,

Ang

2024

Brain Sciences

View full text Add to dashboard Cite

Emotions are a series of subconscious, fleeting, and sometimes elusive manifestations of the human innate system. They play crucial roles in everyday life—influencing the way we evaluate ourselves, our surroundings, and how we interact with our world. To date, there has been an abundance of research on the domains of neuroscience and affective computing, with experimental evidence and neural network models, respectively, to elucidate the neural circuitry involved in and neural correlates for emotion recognition. Recent advances in affective computing neural network models often relate closely to evidence and perspectives gathered from neuroscience to explain the models. Specifically, there has been growing interest in the area of EEG-based emotion recognition to adopt models based on the neural underpinnings of the processing, generation, and subsequent collection of EEG data. In this respect, our review focuses on providing neuroscientific evidence and perspectives to discuss how emotions potentially come forth as the product of neural activities occurring at the level of subcortical structures within the brain’s emotional circuitry and the association with current affective computing models in recognizing emotions. Furthermore, we discuss whether such biologically inspired modeling is the solution to advance the field in EEG-based emotion recognition and beyond.

show abstract

“…The leave-one-out accuracy in the balanced dataset ranged from 58.3 to 87.1, which means there was still a great difference in data distributions across different subjects. Meta-learning may be a way to address this problem in the future [67]. Furthermore, we only performed experiments on one dataset.…”

Section: Challenges and Further Research Directionsmentioning

confidence: 99%

A Connectivity-Aware Graph Neural Network for Real-Time Drowsiness Classification

Zhuang,

Wang,

Chang

et al. 2024

IEEE Trans. Neural Syst. Rehabil. Eng.

View full text Add to dashboard Cite

Drowsy driving is one of the primary causes of driving fatalities. Electroencephalography (EEG), a method for detecting drowsiness directly from brain activity, has been widely used for detecting driver drowsiness in real-time. Recent studies have revealed the great potential of using brain connectivity graphs constructed based on EEG data for drowsy state predictions. However, traditional brain connectivity networks are irrelevant to the downstream prediction tasks. This article proposes a connectivity-aware graph neural network (CAGNN) using a selfattention mechanism that can generate task-relevant connectivity networks via end-to-end training. Our method achieved an accuracy of 72.6% and outperformed other convolutional neural networks (CNNs) and graph generation methods based on a drowsy driving dataset. In addition, we introduced a squeeze-andexcitation (SE) block to capture important features and demonstrated that the SE attention score can reveal the most important feature band. We compared our generated connectivity graphs in the drowsy and alert states and found drowsiness connectivity patterns, including significantly reduced occipital connectivity and interregional connectivity. Additionally, we performed a post hoc interpretability analysis and found that our method could identify drowsiness features such as alpha spindles. Our code is available online 1 .

show abstract

Ultra Efficient Transfer Learning with Meta Update for Cross Subject EEG Classification

Cited by 6 publications

References 21 publications

Comparison of Domain Selection Methods for Multi-Source Manifold Feature Transfer Learning in Electroencephalogram Classification

Comparison of Domain Selection Methods for Multi-Source Manifold Feature Transfer Learning in Electroencephalogram Classification

Review of EEG Affective Recognition with a Neuroscience Perspective

A Connectivity-Aware Graph Neural Network for Real-Time Drowsiness Classification

Contact Info

Product

Resources

About