Removal of EOG and EMG artifacts from EEG using combination of functional link neural network and adaptive neural fuzzy inference system

Hu, Jing; Wang, Chunsheng; Wu, Min; Du, Yuxiao; He, Yong; She, Jinhua

doi:10.1016/j.neucom.2014.09.040

Cited by 73 publications

(28 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…He, and J. She, [13] presented a new approach for removing electrooculogram (EOG) and EMG artifacts from EEG. Proposed approach contains a combination of Adaptive Neural Fuzzy Inference System (ANFIS) and Functional Link Neural Network (FLNN) to construct a filter for enhancing the nonlinear approximation ability of the method.…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Evaluation of Knee Activities Using EMG Signals for Pre-predicting Lower Limb Dystonia Diseases

Balasubramanyam¹,

Kalappan²

2018

IJIES

View full text Add to dashboard Cite

Electromyography (EMG) signal is one of the important tools for the detection of skeletal related muscular information. In this scenario, we analyse the normality and abnormality of knee movements for pre-predicting the knee dystonia diseases. It is characterized by involuntary sustained muscle contractions affecting one or more sides of the body, frequently causing twisting and repetitive movements or abnormal postures. In this paper, EMG based knee dystonia pre-prediction is assessed by employing EMG-Lower Limb dataset. Then, transformation is done by using discrete wavelet Transformation (DWT), which delivers a superior time and frequency localization ability. After transformation, hybrid feature extraction is performed by employing yule-walker, burg's, voltage level (minimum and maximum), Renyi entropy, and Peak-Magnitude to Root Mean Square Ratio (PMRS) for achieving optimal feature subsets and also to reject the redundant and irrelevant features. In most of the existing studies, an individual feature or two different features are combined for extracting the feature values from the acquired signal. In this research, five effective entropy and autoregressive features are combined to obtain more active features. After obtaining the active features, a superior binary classifier: Kernel Nearest Neighbour (KNN) is implemented for classifying the normality and abnormality of knee movements. The experimental outcome proves that the proposed methodology effectively distinguishes the normal and abnormal knee movement in terms of sensitivity, specificity, recall, precision, accuracy and E-rate. The proposed methodology improves the classification accuracy in knee movement detection upto 0.785-1.65% compared to the existing methods.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

“…The general formulas of accuracy, precision and recall for determining normal and abnormal activities of knee movement detection are given in the Eqs. (13), (14) and (15), respectively.…”

Section: Performance Evaluationmentioning

confidence: 99%

Evaluation of Knee Activities Using EMG Signals for Pre-predicting Lower Limb Dystonia Diseases

Balasubramanyam¹,

Kalappan²

2018

IJIES

View full text Add to dashboard Cite

show abstract

“…Through this process, EEG systems would provide correct outputs for their unique and beneficial interface. Even today, many works for detection, classification, and removal of artifacts within observed EEG signals have been reported [20][21][22].…”

Section: Biological Artifactsmentioning

confidence: 99%

Review of Artifact Rejection Methods for Electroencephalographic Systems

Kanoga¹,

Mitsukura²

2017

Electroencephalography

View full text Add to dashboard Cite

Technologies using electroencephalographic (EEG) signals have been penetrated into public by the development of EEG systems. During EEG system operation, recordings ought to be obtained under no restriction of movement for routine use in the real world. However, the lack of consideration of situational behavior constraints will cause technical/biological artifacts that often mixed with EEG signals and make the signal processing difficult in all respects by ingeniously disguising themselves as EEG components. EEG systems integrating gold standard or specialized device in their processing strategies would appear as daily tools in the future if they are unperturbed to such obstructions. In this chapter, we describe algorithms for artifact rejection in multi-/single-channel. In particular, some existing single-channel artifact rejection methods that will exhibit beneficial information to improve their performance in online EEG systems were summarized by focusing on the advantages and disadvantages of algorithms.

show abstract

“…In terms of image processing, an eye blink activity can be represented by a sequence, S of an image, where Eye blink detection involves the removal of artifacts from the neuro-signals and later classifying it to determine the blinks. Many techniques have been proposed in this regard [10,11]. With the aid of NeuroSky Mindwave Mobile, the method gets easier as Eye Blink Strength (EBS) is returned as an unsigned one byte value with a single API.…”

Section: B 1 Eye Blinksmentioning

confidence: 99%

Assessment of the effect of variations in Eye Blinks on a face recognition algorithm

Shankar

2015

2015 IEEE International Advance Computing Conference (IACC)

View full text Add to dashboard Cite

Currently face recognition has reached a certain degree of maturity when operating under constrained environments. When it comes to real time situations, the system degrades sharply in handling variations like illumination, occlusions, skin tone, cosmetics, image misalignment, age, pose, etc., inherent in the face images acquired. Hence understanding and eliminating the effects of each of these factors is crucial to any face recognition system. This paper deals with studying the effect of variances in the Eye Blink Strengths (EBS) on a face image undergoing face recognition, thereby testing the efficiency of face recognition algorithm. The study makes exclusive usage of Brain Computer Interface (BCI) technology to detect eye blinks and to measure their corresponding EBS values using Electroencephalograph (EEG) device. The face recognition algorithm under test was the amalgamation of Principal Component Analysis (PCA), Local Binary Pattern (LBP) based feature extraction and Support Vector Machine (SVM) based classification. EBS is assessed using an inexpensive, portable, non-invasive EEG device. The efficiency of the face recognition algorithm to withstand the eye blinks with varying degree of EBS values for the given face images was determined. It was found that the proposed methodology of test case generation can be effectively be used to evaluate various other face recognition algorithms against varying eye blinks.

show abstract

Removal of EOG and EMG artifacts from EEG using combination of functional link neural network and adaptive neural fuzzy inference system

Cited by 73 publications

References 26 publications

Evaluation of Knee Activities Using EMG Signals for Pre-predicting Lower Limb Dystonia Diseases

Evaluation of Knee Activities Using EMG Signals for Pre-predicting Lower Limb Dystonia Diseases

Review of Artifact Rejection Methods for Electroencephalographic Systems

Assessment of the effect of variations in Eye Blinks on a face recognition algorithm

Contact Info

Product

Resources

About