Embedded implementation of brain computer interface using FPGA

Aravind, M.; Babu, S. Suresh

doi:10.1109/icett.2016.7873633

Cited by 8 publications

(3 citation statements)

References 8 publications

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“…Other EBCI systems are based on a hardware architecture and they reach a very good performance in terms of the power consumption and run-time. For example, Aravind et al proposed an embedded system that can be used for controlling electrical devices by thinking using EEG signals [ 40 ]. The EEG signal processing chain was composed by band-pass finite impulse response filter, wavelet, and Support Vector Machine (SVM).…”

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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“…EEG signals were developed 8) to extract thinking actions and translate them into electrical commands to develop an embedded BCI system that can be For improved biopotential acquisition and processing, an autonomous embedded BCI system was developed 9) based on an ARM9 processor that can port a real-time operating system for visualevoked potentials. The results show that this application recovered visual evoked potentials using fast Fourier transform (FFT) by extracting frequency-domain features from BCI signals and stimulus-locked interlace correlation (SLIC); thus, a classification method based on EEG signals in the time domain was proposed.…”

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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“…For example, embedded processors are beginning to be used to perform sub-millisecond spike detection and sorting for closedloop experiments in which a stimulus is immediately delivered to the brain whenever a specific neuron fires [3,13]. Similarly, brain machine interfaces are replacing bulky and inconvenient wired connections to large desktops with embedded processors [1,[14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Using branch predictors to predict brain activity in brain-machine implants

Bhattacharjee

2017

Proceedings of the 50th Annual IEEE/ACM International Symposium on Microarchitecture

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A key problem with neuroprostheses and brain monitoring interfaces is that they need extreme energy efficiency. One way of lowering energy is to use the low power modes available on the processors embedded in these devices. We present a technique to predict when neuronal activity of interest is likely to occur, so that the processor can run at nominal operating frequency at those times, and be placed in low power modes otherwise. To achieve this, we discover that branch predictors can also predict brain activity. By performing brain surgeries on awake and anesthetized mice, we evaluate several branch predictors and find that perceptron branch predictors can predict cerebellar activity with accuracies as high as 85%. Consequently, we co-opt branch predictors to dictate when to transition between low power and normal operating modes, saving as much as 59% of processor energy. !"#$ !%#$ Figure 1: (a) The cerebellum, shown in red, is located behind the top of the brain stem and has two hemispheres [47]; (b) a major cerebellar neuron is the Purkinje neuron, imaged here from a mouse brain [48].Figure 2: (a) Block diagram of cerebellar implant (dimensions not drawn to scale) and compared against a coin [2]; (b) the Utah array is used to collect intracellular Purkinje recordings [53, 54].studies on using other branch predictors beyond the ones we study [37,[44][45][46] for neuronal prediction.2 We model a cerebellar monitoring implant. Using architectural, RTL, and circuit modeling, we use the branch predictor to not only predict branches but to also guide energy management. We place the processor in idle low power mode but leave the predictor on to predict brain activity. When the predictor anticipates interesting future cerebellar behavior, it brings the processor back to normal operating mode (where the predictor goes back to being a standard branch predictor). Overall, we save up to 59% of processor energy.An important theme of this work is to ask -since machine learning techniques inspired by the brain have been distilled into hardware predictors (e.g., like the perceptron branch predictor), can we now close the loop and use such predictors to anticipate brain activity and manage resources on neuroprostheses? Our work is a first step in answering this question. Ultimately, this approach can guide not only management of energy, but also other scarce resources.

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Embedded implementation of brain computer interface using FPGA

Cited by 8 publications

References 8 publications

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

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

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

Using branch predictors to predict brain activity in brain-machine implants

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