Optimal feature selection from fNIRS signals using genetic algorithms for BCI

Noori, Farzan Majeed; Naseer, Noman; Qureshi, Nauman Khalid; Nazeer, Hammad; Khan, Rayyan Azam

doi:10.1016/j.neulet.2017.03.013

Cited by 89 publications

(49 citation statements)

References 44 publications

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“…Compared with the traditional fNIRS experiment that analyzes the relationships between the feature value and corresponding behaviors separately, our proposed approach combined two significant features by comparative experiments for a comprehensive evaluation. Compared with the previous studies, the optimal parameter combination of different behaviors may be the same [40][41][42]. However, due to the different target behavior, this parameter combination only has a reference significance and no decisive significance for other behaviors.…”

Section: Discussionmentioning

confidence: 97%

“…To improve the accuracy discrimination of self-rising transfer versus assisted-rising transfer, various feature combinations were used to determine the best combination for an fNIRS-based HRI system. Then and signal peak in statistical features is also an optimal result for distinguishing a mental arithmetic task and right-hand motor-imagery task [41,42]. This is due to the different objects of action generating signals with different characteristics.…”

Section: Discussionmentioning

confidence: 99%

“…This paper demonstrates that the feature combination of the peak and mean is the best among all possible combinations for both ∆c HbO (t) and ∆c HbR (t) signals. Similarly, the classification approach based on the combination of the signal mean and signal peak in statistical features is also an optimal result for distinguishing a mental arithmetic task and right-hand motor-imagery task [41,42]. This is due to the different objects of action generating signals with different characteristics.…”

Section: Discussionmentioning

confidence: 99%

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Quantitative Estimation of Differentiated Mental Fatigue between Self-Rising Transfer and Multiple Welfare Robots-Assisted Rising Transfer

et al. 2020

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The multi-robot system (MRS) and relevant control strategy are a potential and effective approach to assist people with weak motion capability for various forms of assisted living. However, the rising transfer, a frequent and strenuous behavior, and its human-robot interaction (HRI) process with MRS, especially mental state, has never been researched, although it directly determines the user experience and security. In this paper, Functional Near-InfraRed Spectroscopy (fNIRS), a brain imaging technique to perform a continuous measure of the mental state, is introduced to monitor the user’s mental fatigue when implementing a behavior transfer in two difficulty levels assisted by multiple welfare-robots. Twenty-five subjects performed self-rising transfer and multiple welfare robots-assisted rising transfer. After removing physiological noises, six features of oxygenated and deoxygenated hemoglobin (HbO and HbR, respectively) features, which included the mean, slope, variance, peak, skewness, and kurtosis, were calculated. To maximize the distinction of fNIRS between self-rising transfer and assisted-rising transfer (multiple welfare robots assisted rising transfer), the optimal statistical feature combination for linear discriminant analysis (LDA) classification was proposed. In addition, the classification accuracy is regarded as a standard to quantify the difference of mental states between two contrasting behaviors. By fitting the index, we established the mental fatigue model that grows exponentially as the workload increases. Finally, the mental fatigue model is applied to guide the nursing mode of caregivers and the control strategy of the MRS. Our findings disclose that the combinations containing mean and peak values significantly yielded higher classification accuracies for both HbO and HbR than the entire other combinations did, across all the subjects. They effectively quantify mental fatigue to provide an evaluation with a theoretical foundation for enhancing the user experience and optimizing the control strategy of MRS.

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Section: Discussionmentioning

confidence: 97%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Quantitative Estimation of Differentiated Mental Fatigue between Self-Rising Transfer and Multiple Welfare Robots-Assisted Rising Transfer

et al. 2020

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“…GAs have largely been used in image classification, as in [37] and [38], where a GA is applied to find the best features for an apple leaf classification and breast cancer diagnosis, respectively. They are also used for optimal feature selection from fNIR signals [39] and in text classification [40]. The application of GA to fault classification is relatively new.…”

Section: Introductionmentioning

confidence: 99%

Bearing Fault Diagnosis of Induction Motors Using a Genetic Algorithm and Machine Learning Classifiers

Toma

Prosvirin

Kim

2020

Sensors

184

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Efficient fault diagnosis of electrical and mechanical anomalies in induction motors (IMs) is challenging but necessary to ensure safety and economical operation in industries. Research has shown that bearing faults are the most frequently occurring faults in IMs. The vibration signals carry rich information about bearing health conditions and are commonly utilized for fault diagnosis in bearings. However, collecting these signals is expensive and sometimes impractical because it requires the use of external sensors. The external sensors demand enough space and are difficult to install in inaccessible sites. To overcome these disadvantages, motor current signal-based bearing fault diagnosis methods offer an attractive solution. As such, this paper proposes a hybrid motor-current data-driven approach that utilizes statistical features, genetic algorithm (GA) and machine learning models for bearing fault diagnosis. First, the statistical features are extracted from the motor current signals. Second, the GA is utilized to reduce the number of features and select the most important ones from the feature database. Finally, three different classification algorithms namely KNN, decision tree, and random forest, are trained and tested using these features in order to evaluate the bearing faults. This combination of techniques increases the accuracy and reduces the computational complexity. The experimental results show that the three classifiers achieve an accuracy of more than 97%. In addition, the evaluation parameters such as precision, F1-score, sensitivity, and specificity show better performance. Finally, to validate the efficiency of the proposed model, it is compared with some recently adopted techniques. The comparison results demonstrate that the suggested technique is promising for diagnosis of IM bearing faults.

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“…With non-invasive methods, on the other hand, there is no such risk. Non-invasive techniques include electroencephalography (EEG) (Wolpaw et al, 2002; Pfurtscheller et al, 2003; Salvaris and Sepulveda, 2010; Cong et al, 2011, 2015; Jin et al, 2011, 2014, 2015; Choi, 2013; Chen et al, 2015), functional magnetic resonance imaging (fMRI) (Enzinger et al, 2008; Sorger et al, 2009), and functional near-infrared spectroscopy (fNIRS) (Ferrari et al, 1985; Kato et al, 1993; Coyle et al, 2004, 2007; Naito et al, 2007; Naseer and Hong, 2013; Naseer et al, 2014; Noori et al, 2017). Over the course of the past decade, fNIRS-based BCI systems have been the focus of considerable research interest and discussion due to their portability, affordable cost and better temporal resolution relative to fMRI.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Classification Performance of Functional Near-Infrared Spectroscopy- Brain–Computer Interface Using Adaptive Estimation of General Linear Model Coefficients

et al. 2017

Self Cite

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In this paper, a novel methodology for enhanced classification of functional near-infrared spectroscopy (fNIRS) signals utilizable in a two-class [motor imagery (MI) and rest; mental rotation (MR) and rest] brain–computer interface (BCI) is presented. First, fNIRS signals corresponding to MI and MR are acquired from the motor and prefrontal cortex, respectively, afterward, filtered to remove physiological noises. Then, the signals are modeled using the general linear model, the coefficients of which are adaptively estimated using the least squares technique. Subsequently, multiple feature combinations of estimated coefficients were used for classification. The best classification accuracies achieved for five subjects, for MI versus rest are 79.5, 83.7, 82.6, 81.4, and 84.1% whereas those for MR versus rest are 85.5, 85.2, 87.8, 83.7, and 84.8%, respectively, using support vector machine. These results are compared with the best classification accuracies obtained using the conventional hemodynamic response. By means of the proposed methodology, the average classification accuracy obtained was significantly higher (p < 0.05). These results serve to demonstrate the feasibility of developing a high-classification-performance fNIRS-BCI.

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Optimal feature selection from fNIRS signals using genetic algorithms for BCI

Cited by 89 publications

References 44 publications

Quantitative Estimation of Differentiated Mental Fatigue between Self-Rising Transfer and Multiple Welfare Robots-Assisted Rising Transfer

Quantitative Estimation of Differentiated Mental Fatigue between Self-Rising Transfer and Multiple Welfare Robots-Assisted Rising Transfer

Bearing Fault Diagnosis of Induction Motors Using a Genetic Algorithm and Machine Learning Classifiers

Enhancing Classification Performance of Functional Near-Infrared Spectroscopy- Brain–Computer Interface Using Adaptive Estimation of General Linear Model Coefficients

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