Efficient Hardware Implementation of Real-Time Low-Power Movement Intention Detector System Using FFT and Adaptive Wavelet Transform

Chamanzar, Alireza; Shabany, Mahdi; Malekmohammadi, Alireza; Mohammadinejad, Sara

doi:10.1109/tbcas.2017.2669911

Cited by 10 publications

(4 citation statements)

References 21 publications

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“…FFT is a common method used to extract signals in the frequency domain of the VEP. It is a fast method of FT to shorten the operation time [42][43].…”

Section: Feature Extraction and Classificationmentioning

confidence: 99%

The Effect of SOA on An Asynchronous ERP and VEP-Based BCI

Yang

Liu

et al. 2021

IEEE Access

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Our previous study established an asynchronous BCI system by using the oddball paradigm to simultaneously induce event-related potentials (ERPs) and visual evoked potentials (VEPs) (E-V BCIs). We found that stimulus onset asynchrony (SOA) is an important factor for performance since it significantly affects the ERP and VEP. Increasing the SOA increases the ERP, which improves the accuracy of detecting target stimuli. However, a larger SOA leads to a lower VEP frequency, which causes the VEP to have poor accuracy when discriminating between the brain states. How to balance the two potentials and accuracies is a problem. This study established eight SOAs from 100 ms to 375 ms that were composed of different interstimulus intervals and the same stimulus duration of 80 ms. We used a probability-based Fisher linear discriminant analysis (P-FLDA) classifier to calculate the classification accuracies of the ERP-based visual speller, VEP-based brain state discrimination, and E-V BCI. The results show that as the SOA increases, the amplitudes of N200 and P300 increase, and the accuracy also shows an increasing trend. However, the frequency of the VEP and the accuracies of state discrimination show downward trends. The change in accuracies of the E-V BCI system combining these two parts is nonlinear, and the SOA optimal value is 125 ms. The SOA of 125 ms yields the best accuracies of 95.83% and practical bit rate of 57.17 bits/min in the E-V BCI system, which provides a guideline for selecting the SOA to improve the performance.

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“…FFT is a common method used to extract signals in the frequency domain of the VEP. It is a fast method of FT to shorten the operation time [42][43].…”

Section: Feature Extraction and Classificationmentioning

confidence: 99%

The Effect of SOA on An Asynchronous ERP and VEP-Based BCI

Yang

Liu

et al. 2021

IEEE Access

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“…Previous research illustrating ERD as a movement preparation indicator include a study to find a suitable classifier for ERD using data from self-determined reaching movement experiments 51 , development of a novel algorithm for using ERDs to detect hand movement intention using adaptive wavelet transform 52 and using ERDs as model inputs to reduce false positives in a motor imagery for rehabilitation application using a two-phase classifier design 53 . Bereitschaftpotentials have also received attention with advancements made in their understanding and prediction.…”

Section: Related Workmentioning

confidence: 99%

Convolutional spiking neural networks for intent detection based on anticipatory brain potentials using electroencephalogram

Lutes,

Nadendla,

Krishnamurthy

2024

Sci Rep

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Spiking neural networks (SNNs) are receiving increased attention because they mimic synaptic connections in biological systems and produce spike trains, which can be approximated by binary values for computational efficiency. Recently, the addition of convolutional layers to combine the feature extraction power of convolutional networks with the computational efficiency of SNNs has been introduced. This paper studies the feasibility of using a convolutional spiking neural network (CSNN) to detect anticipatory slow cortical potentials (SCPs) related to braking intention in human participants using an electroencephalogram (EEG). Data was collected during an experiment wherein participants operated a remote-controlled vehicle on a testbed designed to simulate an urban environment. Participants were alerted to an incoming braking event via an audio countdown to elicit anticipatory potentials that were measured using an EEG. The CSNN’s performance was compared to a standard CNN, EEGNet and three graph neural networks via 10-fold cross-validation. The CSNN outperformed all the other neural networks, and had a predictive accuracy of 99.06% with a true positive rate of 98.50%, a true negative rate of 99.20% and an F1-score of 0.98. Performance of the CSNN was comparable to the CNN in an ablation study using a subset of EEG channels that localized SCPs. Classification performance of the CSNN degraded only slightly when the floating-point EEG data were converted into spike trains via delta modulation to mimic synaptic connections.

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“…To name only a few, the method of fused empirical mode decomposition and wavelets is applied to detection-location of damage in a truss-type structure [6]; Wavelet transform is used for the pattern recognition for diagnosis, condition monitoring and fault detection [7][8][9]. It is also used to design an algorithm for Brain-computer interfacing [10], to detect the exact onset of chipping of the cutting tool from the workpiece profile [11], to determine the length of piles [12]; Synchrosqueezed wavelet transform is used for global and local health condition assessment of structures [13], for modal parameters identification of smart civil structures [14].…”

Section: Introductionmentioning

confidence: 99%

A Class of Algorithms for Continuous Wavelet Transform Based on the Circulant Matrix

Xin

Yin

2018

Algorithms

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The Continuous Wavelet Transform (CWT) is an important mathematical tool in signal processing, which is a linear time-invariant operator with causality and stability for a fixed scale and real-life application. A novel and simple proof of the FFT-based fast method of linear convolution is presented by exploiting the structures of circulant matrix. After introducing Equivalent Condition of Time-domain and Frequency-domain Algorithms of CWT, a class of algorithms for continuous wavelet transform are proposed and analyzed in this paper, which can cover the algorithms in JLAB and WaveLab, as well as the other existing methods such as the cwt function in the toolbox of MATLAB. In this framework, two theoretical issues for the computation of CWT are analyzed. Firstly, edge effect is easily handled by using Equivalent Condition of Time-domain and Frequency-domain Algorithms of CWT and higher precision is expected. Secondly, due to the fact that linear convolution expands the support of the signal, which parts of the linear convolution are just the coefficients of CWT is analyzed by exploring the relationship of the filters of Frequency-domain and Time-domain algorithms, and some generalizations are given. Numerical experiments are presented to further demonstrate our analyses.

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Efficient Hardware Implementation of Real-Time Low-Power Movement Intention Detector System Using FFT and Adaptive Wavelet Transform

Cited by 10 publications

References 21 publications

The Effect of SOA on An Asynchronous ERP and VEP-Based BCI

The Effect of SOA on An Asynchronous ERP and VEP-Based BCI

Convolutional spiking neural networks for intent detection based on anticipatory brain potentials using electroencephalogram

A Class of Algorithms for Continuous Wavelet Transform Based on the Circulant Matrix

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