553 research outputs found
EEG-Fest: Few-shot based Attention Network for Driver's Vigilance Estimation with EEG Signals
A lack of driver's vigilance is the main cause of most vehicle crashes.
Electroencephalography(EEG) has been reliable and efficient tool for drivers'
drowsiness estimation. Even though previous studies have developed accurate and
robust driver's vigilance detection algorithms, these methods are still facing
challenges on following areas: (a) small sample size training, (b) anomaly
signal detection, and (c) subject-independent classification. In this paper, we
propose a generalized few-shot model, namely EEG-Fest, to improve
aforementioned drawbacks. The EEG-Fest model can (a) classify the query
sample's drowsiness with a few samples, (b) identify whether a query sample is
anomaly signals or not, and (c) achieve subject independent classification. The
proposed algorithm achieves state-of-the-art results on the SEED-VIG dataset
and the SADT dataset. The accuracy of the drowsy class achieves 92% and 94% for
1-shot and 5-shot support samples in the SEED-VIG dataset, and 62% and 78% for
1-shot and 5-shot support samples in the SADT dataset.Comment: Submitted to peer review journal for revie
EEG-Based Emotion Recognition Using Regularized Graph Neural Networks
Electroencephalography (EEG) measures the neuronal activities in different
brain regions via electrodes. Many existing studies on EEG-based emotion
recognition do not fully exploit the topology of EEG channels. In this paper,
we propose a regularized graph neural network (RGNN) for EEG-based emotion
recognition. RGNN considers the biological topology among different brain
regions to capture both local and global relations among different EEG
channels. Specifically, we model the inter-channel relations in EEG signals via
an adjacency matrix in a graph neural network where the connection and
sparseness of the adjacency matrix are inspired by neuroscience theories of
human brain organization. In addition, we propose two regularizers, namely
node-wise domain adversarial training (NodeDAT) and emotion-aware distribution
learning (EmotionDL), to better handle cross-subject EEG variations and noisy
labels, respectively. Extensive experiments on two public datasets, SEED and
SEED-IV, demonstrate the superior performance of our model than
state-of-the-art models in most experimental settings. Moreover, ablation
studies show that the proposed adjacency matrix and two regularizers contribute
consistent and significant gain to the performance of our RGNN model. Finally,
investigations on the neuronal activities reveal important brain regions and
inter-channel relations for EEG-based emotion recognition
Towards Subject Agnostic Affective Emotion Recognition
This paper focuses on affective emotion recognition, aiming to perform in the
subject-agnostic paradigm based on EEG signals. However, EEG signals manifest
subject instability in subject-agnostic affective Brain-computer interfaces
(aBCIs), which led to the problem of distributional shift. Furthermore, this
problem is alleviated by approaches such as domain generalisation and domain
adaptation. Typically, methods based on domain adaptation confer comparatively
better results than the domain generalisation methods but demand more
computational resources given new subjects. We propose a novel framework,
meta-learning based augmented domain adaptation for subject-agnostic aBCIs. Our
domain adaptation approach is augmented through meta-learning, which consists
of a recurrent neural network, a classifier, and a distributional shift
controller based on a sum-decomposable function. Also, we present that a neural
network explicating a sum-decomposable function can effectively estimate the
divergence between varied domains. The network setting for augmented domain
adaptation follows meta-learning and adversarial learning, where the controller
promptly adapts to new domains employing the target data via a few
self-adaptation steps in the test phase. Our proposed approach is shown to be
effective in experiments on a public aBICs dataset and achieves similar
performance to state-of-the-art domain adaptation methods while avoiding the
use of additional computational resources.Comment: To Appear in MUWS workshop at the 32nd ACM International Conference
on Information and Knowledge Management (CIKM) 202
Cognitive Vigilance Enhancement Using Audio Stimulation of Pure Tone at 250 Hz
In this paper, we propose a novel vigilance enhancement method based on audio stimulation of pure tone at 250 Hz. We induced two different levels of vigilance state; vigilance decrement (VD) and vigilance enhancement (VE). The VD state was induced by performing a modified version of the Stroop Color-Word Task (SCWT) for approximately 45 minutes. Likewise, the VE state was induced by incorporating audio stimulation of 250 Hz into the SCWT for 45 minutes. We assessed the levels of vigilance on 20 healthy subjects by utilizing Electroencephalogram (EEG) signals and machine learning. The EEG signals were analyzed using four types of entropies; Approximate Entropy (AE), Sample Entropy (SE), Fuzzy Entropy (FE), and Differential Entropy (DE). We then quantified vigilance levels using statistical analysis and support vector machines (SVM) classifier. We found that the proposed VE method has significantly reduced the reaction time (RT) by 44% and improved the accuracy of target detection by 25%, (p <; 0.001) compared to VD state. Besides, we found that 30 min of audio stimulation has reduced the RT by 32% from the beginning to the end of VE phase of the experiment. The entropy measures show that the temporal profile of the EEG signals has significantly increased with VE. The classification results showed that SVM technique with DE features across all frequency bands can detect VE levels with accuracy varying between (92.10± 02.24)% to (98.32± 01.14)%, sensitivity of (92.50± 02.33)% to (98.66± 01.00)%, and specificity of (91.70± 02.32)% to (97.99± 01.05)%. Results also showed that the classification performance using DE has outperformed the other entropy measures by an average of +8.07%. Our results demonstrate the effectiveness of the proposed 250 Hz audio stimulation method in improving vigilance level and suggest using it for future cognitive enhancement studies
A Fast, Efficient Domain Adaptation Technique for Cross-Domain Electroencephalography(EEG)-Based Emotion Recognition
Electroencephalography (EEG)-based emotion recognition is an important element in psychiatric health diagnosis for patients. However, the underlying EEG sensor signals are always non-stationary if they are sampled from different experimental sessions or subjects. This results in the deterioration of the classification performance. Domain adaptation methods offer an effective way to reduce the discrepancy of marginal distribution. However, for EEG sensor signals, both marginal and conditional distributions may be mismatched. In addition, the existing domain adaptation strategies always require a high level of additional computation. To address this problem, a novel strategy named adaptive subspace feature matching (ASFM) is proposed in this paper in order to integrate both the marginal and conditional distributions within a unified framework (without any labeled samples from target subjects). Specifically, we develop a linear transformation function which matches the marginal distributions of the source and target subspaces without a regularization term. This significantly decreases the time complexity of our domain adaptation procedure. As a result, both marginal and conditional distribution discrepancies between the source domain and unlabeled target domain can be reduced, and logistic regression (LR) can be applied to the new source domain in order to train a classifier for use in the target domain, since the aligned source domain follows a distribution which is similar to that of the target domain. We compare our ASFM method with six typical approaches using a public EEG dataset with three affective states: positive, neutral, and negative. Both offline and online evaluations were performed. The subject-to-subject offline experimental results demonstrate that our component achieves a mean accuracy and standard deviation of 80.46% and 6.84%, respectively, as compared with a state-of-the-art method, the subspace alignment auto-encoder (SAAE), which achieves values of 77.88% and 7.33% on average, respectively. For the online analysis, the average classification accuracy and standard deviation of ASFM in the subject-to-subject evaluation for all the 15 subjects in a dataset was 75.11% and 7.65%, respectively, gaining a significant performance improvement compared to the best baseline LR which achieves 56.38% and 7.48%, respectively. The experimental results confirm the effectiveness of the proposed method relative to state-of-the-art methods. Moreover, computational efficiency of the proposed ASFM method is much better than standard domain adaptation; if the numbers of training samples and test samples are controlled within certain range, it is suitable for real-time classification. It can be concluded that ASFM is a useful and effective tool for decreasing domain discrepancy and reducing performance degradation across subjects and sessions in the field of EEG-based emotion recognition
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