IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
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IEEE Trans Neural Syst Rehabil Eng · Dec 2005
Reliability of signals from a chronically implanted, silicon-based electrode array in non-human primate primary motor cortex.
Multiple-electrode arrays are valuable both as a research tool and as a sensor for neuromotor prosthetic devices, which could potentially restore voluntary motion and functional independence to paralyzed humans. Long-term array reliability is an important requirement for these applications. Here, we demonstrate the reliability of a regular array of 100 microelectrodes to obtain neural recordings from primary motor cortex (MI) of monkeys for at least three months and up to 1.5 years. ⋯ Arm-movement related modulation was common and 66% of all recorded neurons were tuned to reach direction. The ability for the array to record neural signals from parietal cortex was also established. These results demonstrate that neural recordings that can provide movement related signals for neural prostheses, as well as for fundamental research applications, can be reliably obtained for long time periods using a monolithic microelectrode array in primate MI and potentially from other cortical areas as well.
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IEEE Trans Neural Syst Rehabil Eng · Dec 2005
A time-series prediction approach for feature extraction in a brain-computer interface.
This paper presents a feature extraction procedure (FEP) for a brain-computer interface (BCI) application where features are extracted from the electroencephalogram (EEG) recorded from subjects performing right and left motor imagery. Two neural networks (NNs) are trained to perform one-step-ahead predictions for the EEG time-series data, where one NN is trained on right motor imagery and the other on left motor imagery. Features are derived from the power (mean squared) of the prediction error or the power of the predicted signals. ⋯ Linear discriminant analysis (LDA) is used for classification. This FEP is tested on three subjects off-line and classification accuracy (CA) rates range between 88% and 98%. The approach compares favorably to a well-known adaptive autoregressive (AAR) FEP and also a linear AAR model based prediction approach.
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A single-stage, low-noise preamplifier is designed using the concept of noise matching for recordings of neural signal with cuff electrodes. The signal-to-noise ratio is approximately 1.6 times higher than that of a low-noise integrated amplifier (AMP-01) for a cuff impedance of 1.5 komega. The bandwidth is 230 Hz-8.25 kHz (Rs=2 komega), and the common-mode-rejection-ratio is 91.2 dB at 1 kHz.