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This paper presents a method to reduce artifacts from scalp EEG recordings to facilitate seizure diagnosis/detection for epilepsy patients. The proposed method is primarily based on stationary wavelet transform and takes the spectral band of seizure activities (i.e., 0.5–29 Hz) into account to separate artifacts from seizures. Different artifact templates have been simulated to mimic the most commonly...
This paper focuses on biomimetic systems for building advanced neuroscience and neuroprosthetics platform with closed-loop control mechanisms. Works on neural implants consist of four major functional blocks of stimulation, recording, processing, and wireless communication, each of which has different challenges to be overcome. On-chip integration of these fundamental blocks with low power requirements...
This paper presents the design of versatile platform for advanced neuroscience on the high-level brain functions and neural prostheses. The platform enables researchers to record and stimulate brain activities of multiple free behaving animals wirelessly. The platform consists of three major functional blocks of neural interface, wireless communication system, and neural signal processing software,...
A new method was introduced to enhance fMRI data analysis by reproducibility-based ICA. Using this new method, unreliable components were first identified and removed by computing reproducibility index from multiple ICA realizations. The remaining components were further denoised by eliminating known artifacts according to given criteria. The resultant data were enhanced in terms of statistical power...
In order to detect the weak signal contaminated by strong background noise, and simultaneously reduce the cost of hardware system, an effective de-noising system based on Labview software platform is established. In this paper, we designed synchronous averaging algorithm-based virtual instrument programs using to implement de-noising efficiently, and extract signal successfully, which has been verified...
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