A 5 $\mu$W/Channel Spectral Analysis IC for Chronic Bidirectional Brain–Machine Interfaces

Avestruz, Al-Thaddeus; Santa, W.; Carlson, David; Jensen, Randy M.; Stanslaski, Scott; Helfenstine, A.; Denison, Timothy

doi:10.1109/jssc.2008.2006460

Cited by 122 publications

(69 citation statements)

References 48 publications

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“…Since ECoGbased BCI systems widely utilize spectral power density for their inputs [119], frequency band power extraction techniques have been implemented immediately following AFEs avoiding digitization and RF data transmission of whole ECoG raw signals [120], [121].…”

Section: E Integrated Electrocortical Online Data Processingmentioning

confidence: 99%

Silicon-Integrated High-Density Electrocortical Interfaces

Akinin

Park

et al. 2017

Proc. IEEE

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Section: E Integrated Electrocortical Online Data Processingmentioning

confidence: 99%

Silicon-Integrated High-Density Electrocortical Interfaces

Akinin

Park

et al. 2017

Proc. IEEE

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“…Size, power consumption, and carrier frequency are highly constrained due to implantation requirements. Cochlear implants and deep-brain stimulators (DBS) are examples of neuroprostheses with undeniable positive outcomes [3]. On the other hand, devices that target neuroscience research do not necessarily need to be implantable.…”

mentioning

confidence: 99%

An inductively powered scalable 32-channel wireless neural recording system-on-a-chip for neuroscience applications

Lee

Kiani

et al. 2010

2010 IEEE International Solid-State Circuits Conference - (ISSCC)

120

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“…As a summary, the authors have shown, illustrated and verified that this novel proposed system is capable of early prediction of several types of epileptic seizure, with a prediction period average of 14.57 sec, twice that of the stateof-the-art devices [34], with an average accuracy of 72% and a lower FPR than the current system by 75%. However, there are several disadvantages or undeveloped situations which the proposed system at this stage cannot handle.…”

Section: Table 3 Comprehensive Prediction Summarymentioning

confidence: 65%

Early Prediction of Epilepsy Seizures System based on Artificial Immune BCI System

Zaghloul¹,

Elsayed²,

Bayoumi³

2017

IJCA

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Controlling the surrounding world and predicting future events has always seemed like a dream, but that could become a reality using a Brain Computer/Machine Interface (BCI/BMI). Epilepsy is a group of neurological diseases characterized by epileptic seizures. It affects millions of people worldwide, with 80% of cases occurring in developing countries. This can result in accidents and sudden, unexpected death. Seizures can happen undetectably in newborns, comatose, or motor impaired patients, especially due to the fact that many medical personnel are not qualified for EEG signal analysis. Therefore, a portable automated detection and monitoring solution is in high demand. Thus, in this study a system of a wireless wearable adaptive for early prediction of epilepsy seizures is proposed, works via minimally invasive wireless technology paired with an external control device (e.g., a doctors' smartphone), with a higher than standard accuracy (71%) and prediction time (14.56 sec). This novel architecture has not only opened new opportunities for daily usable BCI implementations, but they can also save a life by helping to prevent a seizure's fatal consequences. General TermsBrain Machine Interface, Artificial Immune, Signal Processing.

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A 5 $\mu$W/Channel Spectral Analysis IC for Chronic Bidirectional Brain–Machine Interfaces

Cited by 122 publications

References 48 publications

Silicon-Integrated High-Density Electrocortical Interfaces

Silicon-Integrated High-Density Electrocortical Interfaces

An inductively powered scalable 32-channel wireless neural recording system-on-a-chip for neuroscience applications

Early Prediction of Epilepsy Seizures System based on Artificial Immune BCI System

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