FPGA-Based Rapid Electroencephalography Signal Classification System

Ma, Xunguang; Zheng, Wenkai; Peng, Zujian; Yang, Jimin

doi:10.1109/icait.2019.8935935

Cited by 13 publications

(4 citation statements)

References 11 publications

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“…In Reference [ 47 ], a pure hardware system based on the FPGA for EEG-MI classification is presented. The EEG signals are processed as a series of multi-channel images in the continuous time domain showing the energy changes in the cerebral cortex during the MI of the subjects.…”

Section: Review Of the Embedded Bci Systemsmentioning

confidence: 99%

Embedded Brain Computer Interface: State-of-the-Art in Research

Belwafi

Gannouni

Aboalsamh

2021

Sensors

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There is a wide area of application that uses cerebral activity to restore capabilities for people with severe motor disabilities, and actually the number of such systems keeps growing. Most of the current BCI systems are based on a personal computer. However, there is a tremendous interest in the implementation of BCIs on a portable platform, which has a small size, faster to load, much lower price, lower resources, and lower power consumption than those for full PCs. Depending on the complexity of the signal processing algorithms, it may be more suitable to work with slow processors because there is no need to allow excess capacity of more demanding tasks. So, in this review, we provide an overview of the BCIs development and the current available technology before discussing experimental studies of BCIs.

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Section: Review Of the Embedded Bci Systemsmentioning

confidence: 99%

Embedded Brain Computer Interface: State-of-the-Art in Research

Belwafi

Gannouni

Aboalsamh

2021

Sensors

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“…Even though portability and low latency are key properties in the neural interface domain, we found prospectively few implementations addressing portable computing platforms such as FPGAs or microcontrollers (uCs) rather than PCs, that we reported in Table 1. In [20] a CNN deployed on FPGA is used for decoding in real-time the electroencephalographic (EEG) signal acquired from 10 channels during a two-class motion imagery classification task. The input of the CNN is structured as a series of frames, with each frame representing a one-second segment of the EEG signal across three frequency bands.…”

Section: Related Workmentioning

confidence: 99%

On-FPGA Spiking Neural Networks for End-to-End Neural Decoding

2023

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In the last decades, deep learning neural decoding algorithms have gained momentum in the field of neural interfaces and neural processing systems. However, to be deployed on low-budget portable devices while maintaining real-time operability, these models must withstand strict computational and power limitations. This work presents a spike decoding system implemented on a low-end Zynq-7010 FPGA, which includes a multiplier-less spike detection pipeline and a spiking-neural-network-based decoder mapped in the programmable logic. We tested the system on two publicly available datasets and achieved comparable results with state-of-the-art neural decoders that use more complex deep learning models. The system required 7.36 times fewer parameters than the smallest architecture tested on the same dataset. Moreover, by exploiting the spike sparsity property of the neural signal, the total amount of computations is reduced by about 90% during a test carried out on real recorded data. The low computational complexity of the chosen spike detection setup, combined with the power efficiency of spiking neural networks, makes this prototype a well-suited choice for low-power real-time neural decoding at the edge.INDEX TERMS Neural decoding, spike detection, spiking neural network, FPGA, real-time, low-power.

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“…The classification accuracy reached approximately 70.0%. To improve this system, a field-programmable gate array (FPGA) accelerator system 15) was proposed, which combines both flexibility and reconfigurability of different CNN structures. Applying the synchronous dataflow model to an embedded system and configuring the intellectual property cores of each layer separately, a 16-bit fixed-point CNN was finally used for EEG classification.…”

Section: Review Of Bci Applicationsmentioning

confidence: 99%

Review of Performance Improvement of a Noninvasive Brain-computer Interface in Communication and Motor Control for Clinical Applications

SAITO,

KAMAGATA,

AKASHI

et al. 2023

Juntendo Medical Journal

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Brain-computer interfaces (BCI) enable direct communication between the brain and a computer or other external devices.They can extend a person's degree of freedom by either strengthening or substituting the human peripheral working capacity.Moreover, their potential clinical applications in medical fields include rehabilitation, affective computing, communication, and control. Over the last decade, noninvasive BCI systems such as electroencephalogram (EEG) have progressed from simple statistical models to deep learning models, with performance improvement over time and enhanced computational power. However, numerous challenges pertaining to the clinical use of BCI systems remain, e.g., the lack of sufficient data to learn more possible features for robust and reliable classification. However, compared with fields such as computer vision and speech recognition, the training samples in the medical BCI field are limited as they target patients who face difficulty generating EEG data compared with healthy control. Because deep learning models incorporate several parameters, they require considerably more data than other conventional methods. Thus, deep learning models have not been thoroughly leveraged in medical BCI.This study summarizes the state-of-the-art progress of the BCI system over the last decade, highlighting critical challenges and solutions.

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FPGA-Based Rapid Electroencephalography Signal Classification System

Cited by 13 publications

References 11 publications

Embedded Brain Computer Interface: State-of-the-Art in Research

Embedded Brain Computer Interface: State-of-the-Art in Research

On-FPGA Spiking Neural Networks for End-to-End Neural Decoding

Review of Performance Improvement of a Noninvasive Brain-computer Interface in Communication and Motor Control for Clinical Applications

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