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Hybrid Brain-Computer Interfaces (BCI) has shown great promise for neuro-prosthetics and assistive devices in the field of rehabilitation. However, the complexity involved with the system design and time cost for classification of motor tasks is a core problem when we step into clinical applications. To help address this problem, simultaneous measurements of Electroencephalography (EEG) and functional...
The ability to allow subjects, including paralyzed patients, to perform a task using brain-computer interfaces has seen a rapid and growing success. Surprisingly, however, it is still not known how far such performance can be improved - especially in cases of long term amputation where both efferent and afferent functions are abolished and may lead to deterioration of the relevant brain representations...
Deep brain stimulation (DBS) is a common therapy for the treatment of essential tremor (ET). Currently, this technology continuously delivers stimulation to deep brain regions to mitigate symptoms. Closed-loop DBS aims to deliver stimulation only when symptoms are present, thus improving battery life and decreasing potential side effects. In this study, we used an investigational DBS device implanted...
Working memory processing is central for higher-order cognitive functions. Although the ability to access and extract working memory load has been proven feasible, the temporal resolution is low and cross-task generalization is poor. In this study, EEG oscillatory activity was recorded from sixteen healthy subjects while they performed two versions of the visual n-back task. Observed effects in the...
As a new biometric, the Electroencephalogram (EEG) signal has the advantages of invisibility, non-clonability, and non-coercion compare to traditional biometrics. However, the real-time and stability are the difficulties that the current EEG-based person authentication systems face. In this paper, we design a real-time and stable person authentication system using EEG signals, which are elicited by...
This paper investigates whether the movement intent of an amputee can be detected and classified in real-time as the individual moved his/her phantom hand. We present a method to detect movement intent using neural signals from the peripheral nervous system (PNS). In addition, we classify eight types of individual hand movements using 300 ms signal segments beginning with our detected starting time...
Brain waves contain fundamental information about cortical activity: signal power within certain frequency bands, which is exploited by a variety of Brain-Computer Interface applications. For real-time systems, these features must be estimated as quickly as possible while maintaining high signal fidelity. Here, we present a statistically optimal signal processing framework for real-time bandpower...
In this work we propose an energy-efficient, implantable, real-time, blind Adaptive Stimulation Artifact Rejection (ASAR) engine. This enables concurrent neural stimulation and recording for state-of-the-art closed-loop neuromodulation systems. Two engines, implemented in 40nm CMOS, achieve convergence of <42µs for Spike ASAR and <167µs for LFP ASAR, and can attenuate artifacts up to 100mVp-p...
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