WIMAGINE: A wireless, low power, 64-channel ECoG recording platform for implantable BCI applications

Charvet, Guillaume; Foerster, M.; Filipe, Sabine; Porcherot, J.; Beche, J.-F.; Guillemaud, R.; Audebert, P.; Regis, G.; Zongo, B.; Robinet, S.; Condemine, C.; Tetu, Y.; Sauter, F.; Mestais, C.; Benabid, Alim‐Louis

doi:10.1109/ner.2011.5910560

Cited by 10 publications

(5 citation statements)

References 11 publications

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“…In the simulation, about 80% of correct event detections were achieved (TPR = 78 ± 13% on average), while the level of the false activations was acceptably low (FPR = 0.44 ± 0.24% on average). At least part of FP resulted from artifacts produced along the recording chain (connectors and so on) and can be eliminated using wireless data transfer (Charvet et al 2011). The increase of false activations in the latest recordings can be explained by degradation of the electrodes.…”

Section: Discussionmentioning

confidence: 99%

“…In addition the quality of signals registered from implanted electrodes decays over time due to scar tissue formation, more for microelectrodes than for cortical (epidural or subdural) electrodes. However, promising results from new designs of matrix of ECoG electrodes for long-term registration of neuronal activity were reported recently (Charvet et al 2011, Ejserholm et al 2011, Hirata et al 2010. After all, the ECoG-based BCI was considered in our study to be the most adapted to clinical applications for individuals with severe motor disability (quadriplegic subjects).…”

Section: Introductionmentioning

confidence: 98%

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IterativeN-way partial least squares for a binary self-paced brain–computer interface in freely moving animals

et al. 2011

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In this paper a tensor-based approach is developed for calibration of binary self-paced brain-computer interface (BCI) systems. In order to form the feature tensor, electrocorticograms, recorded during behavioral experiments in freely moving animals (rats), were mapped to the spatial-temporal-frequency space using the continuous wavelet transformation. An N-way partial least squares (NPLS) method is applied for tensor factorization and the prediction of a movement intention depending on neuronal activity. To cope with the huge feature tensor dimension, an iterative NPLS (INPLS) algorithm is proposed. Computational experiments demonstrated the good accuracy and robustness of INPLS. The algorithm does not depend on any prior neurophysiological knowledge and allows fully automatic system calibration and extraction of the BCI-related features. Based on the analysis of time intervals preceding the BCI events, the calibration procedure constructs a predictive model of control. The BCI system was validated by experiments in freely moving animals under conditions close to those in a natural environment.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

IterativeN-way partial least squares for a binary self-paced brain–computer interface in freely moving animals

et al. 2011

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“…In Table 4 , the battery-operated COTS wireless devices mentioned in [ 47 , 48 ] utilize sleep mode functionality to conserve battery life while the NXP COTS [ 49 ] ultra-high frequency (UHF) radio frequency identification (RFID) chip remains off until powered by an RFID reader. In Table 4 , the Ucode7 is significantly low power compared to ZL70101 and CC2541, as it is a battery-free RFID chip.…”

Section: Essential Building Blocks Of Cots-enabled Iwmdsmentioning

confidence: 99%

“…Figure 5 a shows the ZL770101 chip used as a wireless data link module in a WIMAGINE neurological signal capture device. The basic architecture of WIMAGINE includes 64 platinum electrodes of 4.5 mm pitch, two CINESIC32 ASIC and an MSP430 ultra-low-power microcontroller [ 47 ]. The electronic environment around the CINESIC32 ASICs is implemented using COTS components.…”

Section: Essential Building Blocks Of Cots-enabled Iwmdsmentioning

confidence: 99%

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Commercial Off-the-Shelf Components (COTS) in Realizing Miniature Implantable Wireless Medical Devices: A Review

Khan

Mugisha

Tsiamis

et al. 2022

Sensors

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Over the past decade, there has been exponential growth in the per capita rate of medical patients around the world, and this is significantly straining the resources of healthcare institutes. Therefore, the reliance on smart commercial off-the-shelf (COTS) implantable wireless medical devices (IWMDs) is increasing among healthcare institutions to provide routine medical services, such as monitoring patients’ physiological signals and the remote delivery of therapeutic drugs. These smart COTS IWMDs reduce the necessity of recurring visits of patients to healthcare institutions and also mitigate physical contact, which can minimize the possibility of any potential spread of contagious diseases. Furthermore, the devices provide patients with the benefit of recuperating in familiar surroundings. As such, low-cost, ubiquitous COTS IWMDs have engendered the proliferation of telemedicine in healthcare to provide routine medical services. In this paper, a review work on COTS IWMDs is presented at a macro level to discuss the history of IWMDs, different networked COTS IWMDs, health and safety regulations of COTS IWMDs and the importance of organized procurement. Furthermore, we discuss the basic building blocks of IWMDs and how COTS components can contribute to build these blocks over widely researched custom-built application-specific integrated circuits.

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Implantable Monitoring System for Rodents

Kilinc

Dehollain

Maloberti

2016

Analog Circuits and Signal Processing

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WIMAGINE: A wireless, low power, 64-channel ECoG recording platform for implantable BCI applications

Cited by 10 publications

References 11 publications

IterativeN-way partial least squares for a binary self-paced brain–computer interface in freely moving animals

IterativeN-way partial least squares for a binary self-paced brain–computer interface in freely moving animals

Commercial Off-the-Shelf Components (COTS) in Realizing Miniature Implantable Wireless Medical Devices: A Review

Implantable Monitoring System for Rodents

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