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1. Seizure Detection Using Power Spectral Density via Hyperdimensional Computing

   https://doi.org/10.1109/ICASSP39728.2021.9414083  

   Ge, Lulu; Parhi, Keshab K. (June 2021, Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing)

   null (Ed.)

   Hyperdimensional (HD) computing holds promise for classifying two groups of data. This paper explores seizure detection from electroencephalogram (EEG) from subjects with epilepsy using HD computing based on power spectral density (PSD) features. Publicly available intra-cranial EEG (iEEG) data collected from 4 dogs and 8 human patients in the Kaggle seizure detection contest are used in this paper. This paper explores two methods for classification. First, few ranked PSD features from small number of channels from a prior classification are used in the context of HD classification. Second, all PSD features extracted from all channels are used as features for HD classification. It is shown that for about half the subjects small number features outperform all features in the context of HD classification, and for the other half, all features outperform small number of features. HD classification achieves above 95% accuracy for six of the 12 subjects, and between 85-95% accuracy for 4 subjects. For two subjects, the classification accuracy using HD computing is not as good as classical approaches such as support vector machine classifiers. 

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2. Reconfigurable mixed-kernel heterojunction transistors for personalized support vector machine classification

   https://doi.org/10.1038/s41928-023-01042-7  

   Yan, Xiaodong; Qian, Justin H; Ma, Jiahui; Zhang, Aoyang; Liu, Stephanie E; Bland, Matthew P; Liu, Kevin J; Wang, Xuechun; Sangwan, Vinod K; Wang, Han; et al (November 2023, Nature Electronics)

   Advances in algorithms and low-power computing hardware imply that machine learning is of potential use in off-grid medical data classification and diagnosis applications such as electrocardiogram interpretation. However, although support vector machine algorithms for electrocardiogram classification show high classification accuracy, hardware implementations for edge applications are impractical due to the complexity and substantial power consumption needed for kernel optimization when using conventional complementary metal–oxide–semiconductor circuits. Here we report reconfigurable mixed-kernel transistors based on dual-gated van der Waals heterojunctions that can generate fully tunable individual and mixed Gaussian and sigmoid functions for analogue support vector machine kernel applications. We show that the heterojunction-generated kernels can be used for arrhythmia detection from electrocardiogram signals with high classification accuracy compared with standard radial basis function kernels. The reconfigurable nature of mixed-kernel heterojunction transistors also allows for personalized detection using Bayesian optimization. A single mixed-kernel heterojunction device can generate the equivalent transfer function of a complementary metal–oxide–semiconductor circuit comprising dozens of transistors and thus provides a low-power approach for support vector machine classification applications. 

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3. MMM: Machine Learning-Based Macro-Modeling for Linear Analog ICs and ADC/DACs

   Yishuang Lin, Yaguang Li (January 2023, Proceedings of the ACM/IEEE Workshop on Machine Learning for CAD)

   Performance modeling is a key bottleneck for analog design automation. Although machine learning-based models have advanced the state-of-the-art, they have so far suffered from huge data preparation cost, very limited reusability, and inadequate accuracy for large circuits. We introduce ML-based macro-modeling techniques to mitigate these problems for linear analog ICs and ADC/DACs. On representative testcases, our method achieves more than 1700× speedup for data preparation and remarkably smaller model errors compared to recent ML approaches. It also attains 3600× acceleration over SPICE simulation with very small errors and reduces data preparation time for an ADC design from 40 days to 9.6 hours. 

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4. Efficient Epileptic Seizure Type Classification Using Hyperdimensional Computing

   Estiri, Seyedeh_Newsha; Daoud, Hisham; Najafi, M Hassan; Bayoumi, Magdy (October 2024, -)

   Precise seizure identification plays a vital role in understanding cortical connectivity and informing treatment decisions. Yet, the manual diagnostic methods for epileptic seizures are both labor-intensive and highly specialized. In this study, we propose a Hyperdimensional Computing (HDC) classifier for accurate and efficient multi-type seizure classification. Despite previous seizure analysis efforts using HDC being limited to binary detection (seizure or no seizure), our work breaks new ground by utilizing HDC to classify seizures into multiple distinct types. HDC offers significant advantages, such as lower memory requirements, a reduced hardware footprint for wearable devices, and decreased computational complexity. Due to these attributes, HDC can be an alternative to traditional machine learning methods, making it a practical and efficient solution, particularly in resource-limited scenarios or applications involving wearable devices. We evaluated the proposed technique on the latest version of TUH EEG Seizure Corpus (TUSZ) dataset and the evaluation result demonstrate noteworthy performance, achieving a weighted F1 score of 94.6%. This outcome is in line with, or even exceeds, the performance achieved by the state-ofthe-art traditional machine learning methods. 

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5. A First Look at the Security of EEG-based Systems and Intelligent Algorithms under Physical Signal Injections

   Md Imran Hossen, Yazhou Tu (July 2023, Proceedings of the 2023 Secure and Trustworthy Deep Learning Systems Workshop (SecTL ’23))

   Electroencephalography (EEG) based systems utilize machine learning (ML) and deep learning (DL) models in various applications such as seizure detection, emotion recognition, cognitive workload estimation, and brain-computer interface (BCI). However, the security and robustness of such intelligent systems under analog-domain threats have received limited attention. This paper presents the first demonstration of physical signal injection attacks on ML and DL models utilizing EEG data. We investigate how an adversary can degrade the performance of different models by non-invasively injecting signals into EEG recordings. We show that the attacks can mislead or manipulate the models and diminish the reliability of EEG-based systems. Overall, this research sheds light on the need for more trustworthy physiological-signal-based intelligent systems in the healthcare field and opens up avenues for future work. 

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