Adaptive filtering method for EMG signal using bounded range artificial bee colony algorithm

Verma, Amit; Singh, Yashvir; Gupta, Bhumika

doi:10.1007/s13534-017-0056-x

Cited by 11 publications

(3 citation statements)

References 23 publications

(53 reference statements)

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“…Hence, optimization plays a very important role in this case. Adaptive filtering serves to be beneficial in this regard as noise cancelers (Ahirwal et al, 2012 , 2013 , 2014 ). The authors in Ahirwal et al ( 2014 ) show that through ABC optimization, the performance of adaptive filtering can be enhanced as compared to the conventional LMS and RLS filtering.…”

Section: Formulation Of Optimization Problems In Bcimentioning

confidence: 99%

“…Adaptive filtering serves to be beneficial in this regard as noise cancelers (Ahirwal et al, 2012 , 2013 , 2014 ). The authors in Ahirwal et al ( 2014 ) show that through ABC optimization, the performance of adaptive filtering can be enhanced as compared to the conventional LMS and RLS filtering. The objective function defined in Ahirwal et al ( 2014 ) is the minimization of the mean squared error by selecting optimal weights in the adaptive filter.…”

Section: Formulation Of Optimization Problems In Bcimentioning

confidence: 99%

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Formulation of the Challenges in Brain-Computer Interfaces as Optimization Problems—A Review

Fathima¹,

Kore

2021

Front. Neurosci.

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Electroencephalogram (EEG) is one of the common modalities of monitoring the mental activities. Owing to the non-invasive availability of this system, its applicability has seen remarkable developments beyond medical use-cases. One such use case is brain-computer interfaces (BCI). Such systems require the usage of high resolution-based multi-channel EEG devices so that the data collection spans multiple locations of the brain like the occipital, frontal, temporal, and so on. This results in huge data (with high sampling rates) and with multiple EEG channels with inherent artifacts. Several challenges exist in analyzing data of this nature, for instance, selecting the optimal number of EEG channels or deciding what best features to rely on for achieving better performance. The selection of these variables is complicated and requires a lot of domain knowledge and non-invasive EEG monitoring, which is not feasible always. Hence, optimization serves to be an easy to access tool in deriving such parameters. Considerable efforts in formulating these issues as an optimization problem have been laid. As a result, various multi-objective and constrained optimization functions have been developed in BCI that has achieved reliable outcomes in device control like neuro-prosthetic arms, application control, gaming, and so on. This paper makes an attempt to study the usage of optimization techniques in formulating the issues in BCI. The outcomes, challenges, and major observations of these approaches are discussed in detail.

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Section: Formulation Of Optimization Problems In Bcimentioning

confidence: 99%

Section: Formulation Of Optimization Problems In Bcimentioning

confidence: 99%

Formulation of the Challenges in Brain-Computer Interfaces as Optimization Problems—A Review

Fathima¹,

Kore

2021

Front. Neurosci.

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“…To sum up, many scholars have explored EMG signal processing, especially in terms of collection principle, signal preprocessing, and signal feature analysis [22][23][24][25][26][27]. However, there is a severe lacking of studies on pattern recognition based on effective signal, and evaluation of limb rehabilitation status.…”

Section: Introductionmentioning

confidence: 99%

Identification and Analysis of Limb Rehabilitation Signal Based on Wavelet Transform

Zhang¹,

Zou²,

Ma³

2021

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The development of science and technology has promoted the extensive application of surface electromyography (sEMG) collection technique in real-time exercise testing, assistive judgment of rehabilitation therapy, and assessment of intelligent artificial limb application. However, there is a severe lacking of studies on pattern recognition based on effective signal, and evaluation of limb rehabilitation status. To make up for the gap, this paper explores the identification and analysis of limb rehabilitation signal based on wavelet transform. Specifically, the authors detailed the basic flow of sEMG signal generation in motor unit during limb rehabilitation exercise, and proposed a limb EMG pattern recognition method. Then, support vector machine (SVM) was selected to recognize the pattern of the EMG signal extracted from the limb rehabilitation exercise of patients, and to judge the rehabilitation status. Finally, wavelet thresholding was combined with total variation denoising (TVD) to effectively remove the noise from EMG signal. The proposed method was proved effective through experiments.

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Optimized Convolutional Neural Network-Based Classification of Arrhythmia Disease Using ECG Signals

Sharma

Dinkar

Deep

2021

Advances in Intelligent Systems and Computing

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Adaptive filtering method for EMG signal using bounded range artificial bee colony algorithm

Cited by 11 publications

References 23 publications

Formulation of the Challenges in Brain-Computer Interfaces as Optimization Problems—A Review

Formulation of the Challenges in Brain-Computer Interfaces as Optimization Problems—A Review

Identification and Analysis of Limb Rehabilitation Signal Based on Wavelet Transform

Optimized Convolutional Neural Network-Based Classification of Arrhythmia Disease Using ECG Signals

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