A low-power area-efficient compressive sensing approach for multi-channel neural recording

Shoaran, Mahsa; Lopez, Mariazel Maqueda; Pasupureddi, Vijaya Sankara Rao; Leblebici, Yusuf; Schmid, Alexandre

doi:10.1109/iscas.2013.6572310

Cited by 11 publications

(7 citation statements)

References 8 publications

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“…As shown in Figure 14, a CS encoder can be implemented in both an analog and a digital domain. Figure 14a demonstrates the block diagram of an analog implementation of a CS encoder presented in [72,94,105,106]. The sparsity of the EEG signal in the Gabor domain is utilized in [94] and the design in [72,105,106] exploits the spatial sparsity of the iEEG signals recorded from the electrodes of the sensor array.…”

Section: Data Compressionmentioning

confidence: 99%

Multi-Channel Neural Recording Implants: A Review

Noshahr

Nabavi

Sawan

2020

Sensors

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The recently growing progress in neuroscience research and relevant achievements, as well as advancements in the fabrication process, have increased the demand for neural interfacing systems. Brain-machine interfaces (BMIs) have been revealed to be a promising method for the diagnosis and treatment of neurological disorders and the restoration of sensory and motor function. Neural recording implants, as a part of BMI, are capable of capturing brain signals, and amplifying, digitizing, and transferring them outside of the body with a transmitter. The main challenges of designing such implants are minimizing power consumption and the silicon area. In this paper, multi-channel neural recording implants are surveyed. After presenting various neural-signal features, we investigate main available neural recording circuit and system architectures. The fundamental blocks of available architectures, such as neural amplifiers, analog to digital converters (ADCs) and compression blocks, are explored. We cover the various topologies of neural amplifiers, provide a comparison, and probe their design challenges. To achieve a relatively high SNR at the output of the neural amplifier, noise reduction techniques are discussed. Also, to transfer neural signals outside of the body, they are digitized using data converters, then in most cases, the data compression is applied to mitigate power consumption. We present the various dedicated ADC structures, as well as an overview of main data compression methods.Sensors 2020, 20, 904 2 of 29 an invasive method, records proper signals. However, the disadvantage of this is not being able to record deep in the brain and they average the activity of thousands of neurons [11]. Invasive techniques have been utilized by some BMIs. This is because recording single action potentials from neurons in distributed, functionally-linked ensembles are necessary for the most accurate readout of neural activities [7]. Therefore, increasing the spatial resolution and the number of electrodes are essential for developing BMI.The implementation of a neural recording implant is multi-disciplinary, as it involves the various scientific fields such as electronics, medical, materials, electrodes, and system integration. Increasing the number of electrodes and, consequently, the number of channels (in the range of thousands), creates new challenges for neural recording in the various fields mentioned. Microelectrode technology is not appropriate for these large-scale recordings [12]. Recently, Neuralink Company has built arrays of small and flexible electrodes (3072 electrodes per array), which have enabled thousands of channel recordings [13]. In the microelectronics field, large-scale recordings create many challenges with regards to decreasing the power consumption and chip area.In the design of the neural recording implants, the two constraints, the power consumption and chip area, should be addressed. Implantable circuits should consume very low power to avoid any damage to the surrounding tissue due to gen...

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Section: Data Compressionmentioning

confidence: 99%

Multi-Channel Neural Recording Implants: A Review

Noshahr

Nabavi

Sawan

2020

Sensors

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“…We note that biologists are particularly interested in the frequency of the EOD signal. Hence unlike other CS applications which record analog signals [6]- [8], in the EOD sensing system we record the time domain frequency data. Similar to EMG waveforms [7], the EOD waveform is also sparse in both time and frequency domain.…”

Section: Compressed Sensing Backgroundmentioning

confidence: 99%

On using compressed sensing for efficient transmission & storage of electric organ discharge

Al-Azzawi

Huang

Misra

et al. 2014

2014 IEEE International Symposium on Circuits and Systems (ISCAS)

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In this paper, we present a compressed sensing based framework for a wireless sensor based biological sensing system for recording the frequency of fish electric organ discharge. We investigate the trade-offs between the parameters of compressed sensing, such as sampling matrix, reconstructed signal's signal to noise ratio, and compression ratio. The measured results show that our framework can be used to reduce the transmitted data size by 70% (3x) while maintaining at least 10 dB signal to noise ratio for the reconstructed time domain frequency data. This size reduction with acceptable reconstructed signal quality will help reduce transmission energy and storage requirements for long-term sensing experiments, thus enabling the use of small and low power wireless sensors.

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“…Although CS encoders have been proposed previously [ 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 ], the hardware cost of a matrix-vector multiplier for CS encoder is not small enough for multi-channel neural recording devices, and thus, the reduction of hardware cost is an essential issue. The hardware cost depends on the CS encoder architecture, which can be classified into temporal CS [ 28 , 31 , 32 ] and spatial CS [ 33 , 34 , 35 ]. The temporal CS, especially by a digital implementation, has an advantage in terms of energy efficiency in high-resolution measurement [ 28 ].…”

Section: Introductionmentioning

confidence: 99%

“…However, for multichannel measurement using the temporal CS approach, the digital product-sum operation circuit has to be parallelized for each measurement channel, increasing chip area per measurement channel. In the spatial CS [ 33 , 34 , 35 ], on the other hand, the product-sum operation circuit can be shared with a plurality of channels. Thus, it is suitable for realizing multiple-channel measurement systems.…”

Section: Introductionmentioning

confidence: 99%

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A Time-Domain Analog Spatial Compressed Sensing Encoder for Multi-Channel Neural Recording

Okazawa

Akita

2018

Sensors

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A time-domain analog spatial compressed sensing encoder for neural recording applications is proposed. Owing to the advantage of MEMS technologies, the number of channels on a silicon neural probe array has doubled in 7.4 years, and therefore, a greater number of recording channels and higher density of front-end circuitry is required. Since neural signals such as action potential (AP) have wider signal bandwidth than that of an image sensor, a data compression technique is essentially required for arrayed neural recording systems. In this paper, compressed sensing (CS) is employed for data reduction, and a novel time-domain analog CS encoder is proposed. A simpler and lower power circuit than conventional analog or digital CS encoders can be realized by using the proposed CS encoder. A prototype of the proposed encoder was fabricated in a 180 nm 1P6M CMOS process, and it achieved an active area of 0.0342 mm2-0.166667em/ch. and an energy efficiency of 25.0 pJ/ch.-0.166667em·-0.166667emconv.

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A low-power area-efficient compressive sensing approach for multi-channel neural recording

Cited by 11 publications

References 8 publications

Multi-Channel Neural Recording Implants: A Review

Multi-Channel Neural Recording Implants: A Review

On using compressed sensing for efficient transmission & storage of electric organ discharge

A Time-Domain Analog Spatial Compressed Sensing Encoder for Multi-Channel Neural Recording

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A low-power area-efficient compressive sensing approach for multi-channel neural recording

Cited by 11 publications

References 8 publications

Multi-Channel Neural Recording Implants: A Review

Multi-Channel Neural Recording Implants: A Review

On using compressed sensing for efficient transmission &amp; storage of electric organ discharge

A Time-Domain Analog Spatial Compressed Sensing Encoder for Multi-Channel Neural Recording

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Resources

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On using compressed sensing for efficient transmission & storage of electric organ discharge