Small-Dimension Feature Matrix Construction Method for Decoding Repetitive Finger Movements From Electroencephalogram Signals
نویسندگان
چکیده
منابع مشابه
Decoding Ipsilateral Finger Movements from ECoG Signals in Humans
Several motor related Brain Computer Interfaces (BCIs) have been developed over the years that use activity decoded from the contralateral hemisphere to operate devices. Contralateral primary motor cortex is also the region most severely affected by hemispheric stroke. Recent studies have identified ipsilateral cortical activity in planning of motor movements and its potential implications for ...
متن کاملDecoding finger movements from ECoG signals using Empirical Mode Decomposition
ECoG promises exact localization of brain sources by providing high spatial resolution and good signal quality, thus makes it the premier choice for future BCI applications. Unfortunately decoding these signals is not as straightforward as one would expect. In this work we applied a time-frequency analysis based on Empirical Mode decomposition (EMD) and Adaptive Filtering (AF) to decode and est...
متن کاملDecoding Finger Movements from ECoG Signals Using Switching Linear Models
One of the most interesting challenges in ECoG-based Brain-Machine Interface is movement prediction. Being able to perform such a prediction paves the way to high-degree precision command for a machine such as a robotic arm or robotic hands. As a witness of the BCI community increasing interest toward such a problem, the fourth BCI Competition provides a dataset which aim is to predict individu...
متن کاملTitle: Decoding M1 Neurons during Multiple Finger Movements Abbreviated Title: Decoding Multiple Finger Movements
Number of figures: 4 Number of tables: 0 Number of pages (including front page): 24 Six keywords: neurons, motor cortex, decoding, finger movements, readout, neuronal population Acknowledgments: Ben Hamed and Pouget were supported by NIH MH57823 and research grants from the ONR, and the McDonnell-Pew, Sloan and Schmitt foundations. Schieber was supported by NIH NS27686. Page 1 of 31 Articles in...
متن کاملAsynchronous decoding of finger movements from ECoG signals using long-range dependencies conditional random fields.
OBJECTIVE In this work we propose the use of conditional random fields with long-range dependencies for the classification of finger movements from electrocorticographic recordings. APPROACH The proposed method uses long-range dependencies taking into consideration time-lags between the brain activity and the execution of the motor task. In addition, the proposed method models the dynamics of...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Access
سال: 2020
ISSN: 2169-3536
DOI: 10.1109/access.2020.2982210