Wireless, 32-channel, EEG and epilepsy monitoring system

Modarreszadeh, M.; Schmidt, Robert

doi:10.1109/iembs.1997.756559

Cited by 40 publications

(17 citation statements)

References 1 publication

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“…Since the first demonstration of a single-chip based wireless system for acquisition, processing and telemetry of biomedical data (Song et al 1997), individual components or systems have been developed for recording and wireless transmission of neural data from the central or peripheral nervous system (DeMichele and Troyk 2003;Farshchi et al 2006;Hill and Culler 2002;Irazoqui-Pastor et al 2002;Modarreszadeh and Schmidt 1997;Mohseni and Najafi 2003;Mohseni and Najafi 2004;Nieder 2000;Obeid at el. 2004;Parramon et al 1997;Song et al 2007;Takeuchi and Shimoyama 2004;Wise et al 2004).…”

Section: Integrated Wireless Neural Interface Microsystemsmentioning

confidence: 99%

Integrated wireless neural interface based on the Utah electrode array

Kim

Bhandari

Klein

et al. 2008

Biomed Microdevices

149

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This report presents results from research towards a fully integrated, wireless neural interface consisting of a 100-channel microelectrode array, a custom-designed signal processing and telemetry IC, an inductive power receiving coil, and SMD capacitors. An integration concept for such a device was developed, and the materials and methods used to implement this concept were investigated. We developed a multi-level hybrid assembly process that used the Utah Electrode Array (UEA) as a circuit board. The signal processing IC was flip-chip bonded to the UEA using Au/Sn reflow soldering, and included amplifiers for up to 100 channels, signal processing units, an RF transmitter, and a power receiving and clock recovery module. An under bump metallization (UBM) using potentially biocompatible materials was developed and optimized, which consisted of a sputter deposited Ti/Pt/Au thin film stack with layer thicknesses of 50/150/150 nm, respectively. After flip-chip bonding, an underfiller was applied between the IC and the UEA to improve mechanical stability and prevent fluid ingress in in vivo conditions. A planar power receiving coil fabricated by patterning electroplated gold films on polyimide substrates was connected to the IC by using a custom metallized ceramic spacer and SnCu reflow soldering. The SnCu soldering was also used to assemble SMD capacitors on the UEA. The mechanical properties and stability of the optimized interconnections between the UEA and the IC and SMD components were measured. Measurements included the tape tests to evaluate UBM adhesion, shear testing between the Au/Sn solder bumps and the substrate, and accelerated lifetime testing of the long-term stability for the underfiller material coated with a a-SiC(x):H by PECVD, which was intended as a device encapsulation layer. The materials and processes used to generate the integrated neural interface device were found to yield a robust and reliable integrated package.

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Section: Integrated Wireless Neural Interface Microsystemsmentioning

confidence: 99%

Integrated wireless neural interface based on the Utah electrode array

Kim

Bhandari

Klein

et al. 2008

Biomed Microdevices

149

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show abstract

“…In [25], compression was already applied to ECG signals, and we assume the bitrate of this source also is reduced as a result compared to the figure quoted in [18]. Notice that wireless EEG already exists in [26] with a 32-channel system for epilepsy monitoring rather than a wearable system.…”

Section: Wireless Network Technologymentioning

confidence: 99%

Enabling Cognitive Load‐Aware AR with Rateless Coding on a Wearable Network

Razavi

Fleury

Ghanbari

2008

Advances in Multimedia

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Augmented reality (AR) on a head-mounted display is conveniently supported by a wearable wireless network. If, in addition, the AR display is moderated to take account of the cognitive load of the wearer, then additional biosensors form part of the network. In this paper, the impact of these additional traffic sources is assessed. Rateless coding is proposed to not only protect the fragile encoded video stream from wireless noise and interference but also to reduce coding overhead. The paper proposes a block-based form of rateless channel coding in which the unit of coding is a block within a packet. The contribution of this paper is that it minimizes energy consumption by reducing the overhead from forward error correction (FEC), while error correction properties are conserved. Compared to simple packet-based rateless coding, with this form of block-based coding, data loss is reduced and energy efficiency is improved. Cross-layer organization of piggy-backed response blocks must take place in response to feedback, as detailed in the paper. Compared also to variants of its default FEC scheme, results from a Bluetooth (IEEE 802.15.1) wireless network show a consistent improvement in energy consumption, packet arrival latency, and video quality at the AR display.

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“…Cleveland Medical Devices has commercialized a product [12] in this space, but due to its proprietary nature, there are not many design details available. The device operates on the 902-MHz to 928-MHz band and operates using frequency-shift keying (FSK).…”

Section: Existing Approaches For Developing Neural Recording Techmentioning

confidence: 99%

A TinyOS-Enabled MICA2-Based Wireless Neural Interface

Farshchi

Nuyujukian

Pesterev

et al. 2006

IEEE Trans. Biomed. Eng.

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Abstract-Existing approaches used to develop compact low-power multichannel wireless neural recording systems range from creating custom-integrated circuits to assembling commercial-off-the-shelf (COTS) PC-based components. Custom-integrated-circuit designs yield extremely compact and low-power devices at the expense of high development and upgrade costs and turn-around times, while assembling COTS-PC-technology yields high performance at the expense of large system size and increased power consumption. To achieve a balance between implementing an ultra-compact custom-fabricated neural transceiver and assembling COTS-PC-technology, an overlay of a neural interface upon the TinyOS-based MICA2 platform is described. The system amplifies, digitally encodes, and transmits neural signals real-time at a rate of 9.6 kbps, while consuming less than 66 mW of power. The neural signals are received and forwarded to a client PC over a serial connection. This data rate can be divided for recording on up to 6 channels, with a resolution of 8 bits/sample. This work demonstrates the strengths and limitations of the TinyOS-based sensor technology as a foundation for chronic remote biological monitoring applications and, thus, provides an opportunity to create a system that can leverage from the frequent networking and communications advancements being made by the global TinyOS-development community.

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Wireless, 32-channel, EEG and epilepsy monitoring system

Cited by 40 publications

References 1 publication

Integrated wireless neural interface based on the Utah electrode array

Integrated wireless neural interface based on the Utah electrode array

Enabling Cognitive Load‐Aware AR with Rateless Coding on a Wearable Network

A TinyOS-Enabled MICA2-Based Wireless Neural Interface

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