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A wireless multichannel neural recor...

Obeid, Iyad.

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A wireless multichannel neural recording platform for real-time brain machine interfaces.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	A wireless multichannel neural recording platform for real-time brain machine interfaces./
作者:	Obeid, Iyad.
面頁冊數:	147 p.
附註:	Source: Dissertation Abstracts International, Volume: 66-06, Section: B, page: 3262.
Contained By:	Dissertation Abstracts International66-06B.
標題:	Biology, Neuroscience. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3178704
ISBN:	9780542182525

A wireless multichannel neural recording platform for real-time brain machine interfaces.
Obeid, Iyad.

A wireless multichannel neural recording platform for real-time brain machine interfaces. - 147 p.

Source: Dissertation Abstracts International, Volume: 66-06, Section: B, page: 3262.

Thesis (Ph.D.)--Duke University, 2004.

Recent technological advances attempting to interface prosthetic limbs to the brain have been hampered by a lack of wireless multichannel data acquisition hardware. This work has attempted to fill that void by developing a portable recording platform for up to 16 chronically implanted cortical electrodes. The system consists of (1) an analog "Headstage" integrated circuit for buffering and amplifying the electrode signals, (2) a low power analog front end (AFE) for conditioning and digitizing the neural signals, and (3) a digital back end for transmitting either the raw neural signals, or only the action potential waveforms. The 16-channel Headstage used a non-inverting feedback architecture to achieve tightly matched gains (mu = 1.99) and an input referred noise of 10muVrms. The 16-channel AFE featured variable gain, 4th order Bessel bandpass filtering, and a reference matrix for selectable bipolar recordings. The digital back end consisted of a programmable logic device for detecting spikes, a FIFO memory for queuing the data, and a wearable PC fitted with an 802.11b Ethernet card for transmitting the data over a UDP network protocol. The system measures 5.1 x 8.1 x 12.4cm, weighs 235g (including batteries), and is capable of transmitting 12 channels of 8-bit raw data simultaneously over nine meters. In vivo recordings demonstrated that signals acquired with this system were of similar fidelity to those recorded by a commercial recording system. The spike detector was able to correctly detect over 90% of spikes at signal to noise ratios greater than 2.1. Detection reduced the volume of telemetered data by 97% when the mean spike firing rate was 9.3 spikes/channel/second.

ISBN: 9780542182525Subjects--Topical Terms:

1017680
Biology, Neuroscience.

A wireless multichannel neural recording platform for real-time brain machine interfaces.
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502 $a Thesis (Ph.D.)--Duke University, 2004.
520 $a Recent technological advances attempting to interface prosthetic limbs to the brain have been hampered by a lack of wireless multichannel data acquisition hardware. This work has attempted to fill that void by developing a portable recording platform for up to 16 chronically implanted cortical electrodes. The system consists of (1) an analog "Headstage" integrated circuit for buffering and amplifying the electrode signals, (2) a low power analog front end (AFE) for conditioning and digitizing the neural signals, and (3) a digital back end for transmitting either the raw neural signals, or only the action potential waveforms. The 16-channel Headstage used a non-inverting feedback architecture to achieve tightly matched gains (mu = 1.99) and an input referred noise of 10muVrms. The 16-channel AFE featured variable gain, 4th order Bessel bandpass filtering, and a reference matrix for selectable bipolar recordings. The digital back end consisted of a programmable logic device for detecting spikes, a FIFO memory for queuing the data, and a wearable PC fitted with an 802.11b Ethernet card for transmitting the data over a UDP network protocol. The system measures 5.1 x 8.1 x 12.4cm, weighs 235g (including batteries), and is capable of transmitting 12 channels of 8-bit raw data simultaneously over nine meters. In vivo recordings demonstrated that signals acquired with this system were of similar fidelity to those recorded by a commercial recording system. The spike detector was able to correctly detect over 90% of spikes at signal to noise ratios greater than 2.1. Detection reduced the volume of telemetered data by 97% when the mean spike firing rate was 9.3 spikes/channel/second.
520 $a A study was conducted to determine which spike detection algorithm is best suited for detecting neural spikes in a wearable system with limited computational resources. Detections were scored with a novel cost function that weighed the probabilities of correct detections, the rates of false positive detections, and the computational demands of the detection algorithm relative to the computational capabilities of the system. The results indicated that a simple algorithm, such as taking the absolute value and applying a threshold, is as effective for computationally limited systems as more complex energy or matched filter based detectors.
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