no code implementations • 24 Nov 2023 • Yankun Xu, Jie Yang, Wenjie Ming, Shuang Wang, Mohamad Sawan
An accurate and efficient epileptic seizure onset detection system can significantly benefit patients.
no code implementations • 15 Aug 2023 • Honggui Li, Nahid Md Lokman Hossain, Maria Trocan, Dimitri Galayko, Mohamad Sawan
Five CMISR algorithms are respectively proposed based on the state-of-the-art open-loop MISR algorithms.
no code implementations • 12 Jul 2023 • Xiaomeng Wang, Fengshi Tian, Xizi Chen, Jiakun Zheng, Xuejiao Liu, Fengbin Tu, Jie Yang, Mohamad Sawan, Kwang-Ting Cheng, Chi-Ying Tsui
In this paper, we propose a high-precision SRAM-based CIM macro that can perform 4x4-bit MAC operations and yield 9-bit signed output.
no code implementations • 20 Jan 2023 • Honggui Li, Maria Trocan, Mohamad Sawan, Dimitri Galayko
Closed-loop negative feedback mechanism is extensively utilized in automatic control systems and brings about extraordinary dynamic and static performance.
no code implementations • 4 Jan 2023 • Yankun Xu, Jie Yang, Wenjie Ming, Shuang Wang, Mohamad Sawan
And, a novel multiscale STFT-based feature extraction method combined with 3D-CNN architecture is proposed to accurately capture predictive probabilities of samples.
no code implementations • 28 Nov 2022 • Jinbo Chen, Hui Wu, Jie Yang, Mohamad Sawan
A bio-inspired Neuron-ADC with reconfigurable sampling and static power reduction for biomedical applications is proposed in this work.
no code implementations • 3 Oct 2022 • Di wu, Jie Yang, Mohamad Sawan
In this survey, we assess the eligibility of more than fifty published peer-reviewed representative transfer learning approaches for EMG applications.
no code implementations • 26 Sep 2022 • Chaoming Fang, Ziyang Shen, Fengshi Tian, Jie Yang, Mohamad Sawan
In this design, a compact online learning neuromorphic hardware architecture with ultra-low power consumption designed explicitly for biosignal processing is proposed.
no code implementations • 16 Sep 2022 • Chuanqing Wang, Chaoming Fang, Yong Zou, Jie Yang, Mohamad Sawan
In this paper, we propose an energy-efficient dynamic scenes processing framework (SpikeSEE) that combines a spike representation encoding technique and a bio-inspired spiking recurrent neural network (SRNN) model to achieve intelligent processing and extreme low-power computation for retinal prostheses.
no code implementations • 19 Jul 2022 • Honggui Li, Maria Trocan, Dimitri Galayko, Mohamad Sawan
The proposed method depends on any existing approaches and upgrades their reconstruction performance by adding negative feedback structure.
no code implementations • 8 Jun 2022 • Shiqi Zhao, Jie Yang, Yankun Xu, Mohamad Sawan
Nowadays, several deep learning methods are proposed to tackle the challenge of epileptic seizure prediction.
no code implementations • 27 May 2022 • Jinbo Chen, Mahdi Tarkhan, Hui Wu, Fereidoon Hashemi Noshahr, Jie Yang, Mohamad Sawan
Recent years have seen fast advances in neural recording circuits and systems as they offer a promising way to investigate real-time brain monitoring and the closed-loop modulation of psychological disorders and neurodegenerative diseases.
no code implementations • 26 May 2022 • Jinbo Chen, Fengshi Tian, Jie Yang, Mohamad Sawan
Wearable electrocardiograph (ECG) recording and processing systems have been developed to detect cardiac arrhythmia to help prevent heart attacks.
no code implementations • 29 Apr 2022 • Yankun Xu, Jie Yang, Mohamad Sawan
To identify the preictal region that precedes the onset of seizure, a large number of annotated EEG signals are required to train DL algorithms.
1 code implementation • 20 Apr 2022 • Di wu, Siyuan Li, Jie Yang, Mohamad Sawan
Extensive data labeling on neurophysiological signals is often prohibitively expensive or impractical, as it may require particular infrastructure or domain expertise.
no code implementations • 25 Feb 2022 • Di wu, Jie Yang, Mohamad Sawan
The proposed training scheme significantly improves the performance of patient-specific seizure predictors and bridges the gap between patient-specific and patient-independent predictors.
no code implementations • 26 Oct 2021 • Di wu, Yi Shi, Ziyu Wang, Jie Yang, Mohamad Sawan
Although compressive sensing (CS) can be adopted to compress the signals to reduce communication bandwidth requirement, it needs a complex reconstruction procedure before the signal can be used for seizure prediction.
no code implementations • 17 Aug 2021 • Yankun Xu, Jie Yang, Shiqi Zhao, Hemmings Wu, Mohamad Sawan
Conventional seizure prediction works usually rely on features extracted from Electroencephalography (EEG) recordings and classification algorithms such as regression or support vector machine (SVM) to locate the short time before seizure onset.
no code implementations • 29 Jun 2021 • Honggui Li, Dimitri Galayko, Maria Trocan, Mohamad Sawan
It is evaluated by the experimental results that the proposed autoencoders outperform the classical autoencoders in the performance of image reconstruction.
no code implementations • 5 May 2021 • Ziyu Wang, Jie Yang, Mohamad Sawan
Accurate prediction of epileptic seizures allows patients to take preventive measures in advance to avoid possible injuries.
no code implementations • 25 Feb 2021 • Fengshi Tian, Jie Yang, Shiqi Zhao, Mohamad Sawan
Motivated by the energy-efficient spiking neural networks (SNNs), a neuromorphic computing approach for seizure prediction is proposed in this work.
1 code implementation • 18 Nov 2019 • Armin Najarpour Foroushani, Sujaya Neupane, Pablo De Heredia Pastor, Christopher C. Pack, Mohamad Sawan
The key characteristic of such a system is the ability to discriminate between responses to different positions in the visual field.