Subclass discriminant analysis using dynamic cluster formation for EEG-based brain-computer interface

Oveisi, Farid

doi:10.1109/ner.2009.5109293

Cited by 5 publications

(3 citation statements)

References 8 publications

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“…2.1) Subclass Divisions: How to divide each class into different subclasses is a crucial problem in SDA. Different unsupervised algorithms have been attempted in this field, including K-means clustering [25], [28], dynamic cluster formation [26], Gaussian mixture model [29], hierarchical clustering [24], nearest neighbor (NN) clustering [22], and valley seeking algorithm [27]. However, these unsupervised methods usually clustered data in terms of their inherent similarity, overlooking the physical meanings of the clustered data.…”

Section: Data Processing and Analysismentioning

confidence: 99%

Simultaneous Prediction of Wrist/Hand Motion via Wearable Ultrasound Sensing

Yang

Yan

Fang

et al. 2020

IEEE Trans. Neural Syst. Rehabil. Eng.

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The ability to predict wrist and hand motions simultaneously is essential for natural controls of hand protheses. In this paper, we propose a novel method that includes subclass discriminant analysis (SDA) and principal component analysis for the simultaneous prediction of wrist rotation (pronation/supination) and finger gestures using wearable ultrasound. We tested the method on eight finger gestures with concurrent wrist rotations. Results showed that SDA was able to achieve accurate classification of both finger gestures and wrist rotations under dynamic wrist rotations. When grouping the wrist rotations into three subclasses, about 99.2 ±1.2% of finger gestures and 92.8 ± 1.4% of wrist rotations can be accurately classified. Moreover, we found that the first principal component (PC1) of the selected ultrasound features was linear to the wrist rotation angle regardless of finger gestures. We further used PC1 in an online tracking task for continuous wrist control and demonstrated that a wrist tracking precision (R 2 ) of 0.954 ± 0.012 and a finger gesture classification accuracy of 96.5 ± 1.7% can be simultaneously achieved, with only two minutes of user training. Our proposed simultaneous wrist/hand control scheme is training-efficient and robust, paving the way for musculaturedriven artificial hand control and rehabilitation treatment.Index Terms-Subclass discriminant analysis, principal component analysis, simultaneous wrist/hand control, wearable ultrasound sensing.

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Section: Data Processing and Analysismentioning

confidence: 99%

Simultaneous Prediction of Wrist/Hand Motion via Wearable Ultrasound Sensing

Yang

Yan

Fang

et al. 2020

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…1). The research on SDA usually emphasize the separability of original classes rather than subclasses, ignoring the potential meaning of subclasses [25], [26], [27], [28]. Our previous study showed that the subclass information of each finger gesture can be used to represent the wrist rotation position [22].…”

Section: Machine Learning Algorithmsmentioning

confidence: 99%

Simultaneous Prediction of Wrist and Hand Motions via Wearable Ultrasound Sensing for Natural Control of Hand Prostheses

Yang

Liu

Yin

et al. 2022

IEEE Trans. Neural Syst. Rehabil. Eng.

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Simultaneous prediction of wrist and hand motions is essential for the natural interaction with hand prostheses. In this paper, we propose a novel multi-out Gaussian process (MOGP) model and a multi-task deep learning (MTDL) algorithm to achieve simultaneous prediction of wrist rotation (pronation/supination) 1 and finger gestures for transradial amputees via a wearable ultrasound array. We target six finger gestures with concurrent wrist rotation in four transradial amputees. Results show that MOGP outperforms previously reported subclass discriminant analysis for both predictions of discrete finger gestures and continuous wrist rotation. Moreover, we find that MTDL has the potential to improve the accuracy of finger gesture prediction compared to MOGP and classification-specific deep learning, albeit at the expense of reducing the accuracy of wrist rotation prediction. Extended comparative analysis shows the superiority of ultrasound over surface electromyography. This paper prioritizes exploring the performance of wearable ultrasound on the simultaneous prediction of wrist and hand motions for transradial amputees, demonstrating the potential of ultrasound in future prosthetic control. Our ultrasound-based adaptive prosthetic control dataset (UltraPro) will be released to promote the development of the prosthetic community.Index Terms-Simultaneous wrist and hand motion control, transradial amputees, wearable ultrasound, multi-out Gaussian process, multi-task deep learning, UltraPro dataset.

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“…Therefore, the subspace that maximizes the constrained log-likelihood function in (19) at each EM cycle coincides with the subspace that maximizes the MSDA criterion, where the scatter matrices in (4) are replaced by their weighted equivalent in each EM cycle. The MLE of the true means can now be computed by substituting (23), (32) into (11) and using the computed estimates of ( 16), (35) for S ws and Ψ respectively…”

Section: Em Algorithmmentioning

confidence: 99%

Mixture Subclass Discriminant Analysis

Gkalelis

Mezaris

Kompatsiaris

2011

IEEE Signal Process. Lett.

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In this paper, a theoretical link between mixture subclass discriminant analysis (MSDA) and restricted Gaussian model is first presented, and then two further discriminant analysis (DA) methods, fractional step MSDA (FSMSDA) and kernel MSDA (KMSDA) are proposed. Linking MSDA to an appropriate Gaussian model allows the derivation of a new DA method under the Expectation Maximization (EM) framework (EM-MSDA), that derives simultaneously the discriminant subspace as well as the maximum likelihood estimates. The two other proposed methods generalize MSDA in order to solve problems inherited from conventional discriminant analysis. FSMSDA solves the subclass separation problem, that is, the situation when the dimensionality of the discriminant subspace is strictly smaller than the rank of the inter-between-subclass scatter matrix. This is done by an appropriate weighting scheme and the utilization of an iterative algorithm for preserving useful discriminant directions. On the other hand, KMSDA uses the kernel trick to separate data with nonlinearly separable subclass structure. Extensive experimentation shows that the proposed methods outperform conventional MSDA and other LDA variants.

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Subclass discriminant analysis using dynamic cluster formation for EEG-based brain-computer interface

Cited by 5 publications

References 8 publications

Simultaneous Prediction of Wrist/Hand Motion via Wearable Ultrasound Sensing

Simultaneous Prediction of Wrist/Hand Motion via Wearable Ultrasound Sensing

Simultaneous Prediction of Wrist and Hand Motions via Wearable Ultrasound Sensing for Natural Control of Hand Prostheses

Mixture Subclass Discriminant Analysis

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