Selection of stable features for modeling 4-D affective space from EEG recording

Al-Fahad, Rakib; Yeasin, Mohammed; Abid, Anam; Elahian, Bahareh

doi:10.1109/ijcnn.2017.7965989

Cited by 8 publications

(3 citation statements)

References 32 publications

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“…Hence Lasso was used for stability selection. Applying Randomized Lasso many times and looking for variables that are chosen is a very powerful procedure tool to select consistent or stable features (Al-Fahad et al, 2017;Meinshausen and Bühlmann, 2006;Shah and Samworth, 2013;Tibshirani, 1996). Despite its simplicity, it is consistent for variable selection even though the 'neighborhood stability' condition is violated.…”

Section: Feature Selectionmentioning

confidence: 99%

Decoding of single-trial EEG reveals unique states of functional brain connectivity that drive rapid speech categorization decisions

2020

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Categorical perception (CP) is an inherent property of speech perception. The response time (RT) of listeners' perceptual speech identification is highly sensitive to individual differences. While the neural correlates of CP have been well studied in terms of the regional contributions of the brain to behavior, functional connectivity patterns that signify individual differences in listeners' speed (RT) for speech categorization is less clear. To address these questions, we applied several computational approaches to the EEG, including graph mining, machine learning (i.e., support vector machine), and stability selection to investigate the unique brain states (functional neural connectivity) that predict the speed of listeners' behavioral decisions. We infer that (i) the listeners' perceptual speed is directly related to dynamic variations in their brain connectomics, (ii) global network assortativity and efficiency distinguished fast, medium, and slow RT, (iii) the functional network underlying speeded decisions increases in negative assortativity (i.e., became disassortative) for slower RTs, (iv) slower categorical speech decisions cause excessive use of neural resources and more aberrant information flow within the CP circuitry, (v) slower responders tended to utilize functional brain networks excessively (or inappropriately) whereas fast responders (with lower global efficiency) utilized the same neural pathways but with more restricted organization. Our results showed that neural classifiers (SVM) coupled with stability selection correctly classify behavioral RTs from functional connectivity alone with over 92% accuracy (AUC=0.9). Our results corroborate previous studies by supporting the engagement of similar temporal (STG), parietal, motor, and prefrontal regions in CP using an entirely data-driven approach.

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Section: Feature Selectionmentioning

confidence: 99%

Decoding of single-trial EEG reveals unique states of functional brain connectivity that drive rapid speech categorization decisions

2020

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“…In conjunction with other techniques such as SVR for feature selection based on twin support vector regression [2]; with SVM and Bayes for categorical classifications [3]. For the modeling of emotions and affective states from EEG, combining RFE with Random Forest (RF), Support Vector Regression (SVR), Tree-based bagging [4]. In identifying features for football game earnings forecast, combining it with were Gradient Boosting and Random Forest [5].…”

Section: Introductionmentioning

confidence: 99%

Selection of Characteristics by Hybrid Method: RFE, Ridge, Lasso, and Bayesian for the Power Forecast for a Photovoltaic System

et al. 2021

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Currently, the generation of alternative energy from solar radiation with photovoltaic systems is growing, its efficiency depends on internal variables such as powers, voltages, currents; as well as external variables such as temperatures, irradiance, and load. To maximize performance, this research focused on the application of regularization techniques in a multiparametric linear regression model to predict the active power levels of a photovoltaic system from 14 variables that model the system under study. These variables affect the prediction to some degree, but some of them do not have so much preponderance in the final forecast, so it is convenient to eliminate them so that the processing cost and time are reduced. For this, we propose a hybrid selection method: first we apply the elimination of Recursive Feature Elimination (RFE) within the selection of subsets and then to the obtained results we apply the following contraction regularization methods: Lasso, Ridge and Bayesian Ridge; then the results were validated demonstrating linearity, normality of the error terms, without autocorrelation and homoscedasticity. All four prediction models had an accuracy greater than 99.97%. Training time was reduced by 71% and 36% for RFE-Ridge and RFE-OLS respectively. The variables eliminated with RFE were Total Energy, Daily Energy, and Irradiance, while the variable eliminated by Lasso was: "Frequency". In all cases we see that the root mean square errors were reduced for RFE. Lasso by 0.15% while for RFE-Bayesian Ridge by 0.06%.

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“…In addition to emotion classification studies which are based on a limited number of pre-defined emotional categories, several studies have performed EEG-based or electrocorticography (ECoG)-based emotion or mood state regression/correlation analyses to recognize small-scale emotional changes [ 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 ]. McFarland and colleagues performed canonical correlation analysis (CCA) to predict participants’ emotional states from the sensor-level features of EEG recorded during affective picture viewing [ 22 ].…”

Section: Introductionmentioning

confidence: 99%

Independent Components of EEG Activity Correlating with Emotional State

et al. 2020

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Among brain-computer interface studies, electroencephalography (EEG)-based emotion recognition is receiving attention and some studies have performed regression analyses to recognize small-scale emotional changes; however, effective brain regions in emotion regression analyses have not been identified yet. Accordingly, this study sought to identify neural activities correlating with emotional states in the source space. We employed independent component analysis, followed by a source localization method, to obtain distinct neural activities from EEG signals. After the identification of seven independent component (IC) clusters in a k-means clustering analysis, group-level regression analyses using frequency band power of the ICs were performed based on Russell’s valence–arousal model. As a result, in the regression of the valence level, an IC cluster located in the cuneus predicted both high- and low-valence states and two other IC clusters located in the left precentral gyrus and the precuneus predicted the low-valence state. In the regression of the arousal level, the IC cluster located in the cuneus predicted both high- and low-arousal states and two posterior IC clusters located in the cingulate gyrus and the precuneus predicted the high-arousal state. In this proof-of-concept study, we revealed neural activities correlating with specific emotional states across participants, despite individual differences in emotional processing.

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Selection of stable features for modeling 4-D affective space from EEG recording

Cited by 8 publications

References 32 publications

Decoding of single-trial EEG reveals unique states of functional brain connectivity that drive rapid speech categorization decisions

Decoding of single-trial EEG reveals unique states of functional brain connectivity that drive rapid speech categorization decisions

Selection of Characteristics by Hybrid Method: RFE, Ridge, Lasso, and Bayesian for the Power Forecast for a Photovoltaic System

Independent Components of EEG Activity Correlating with Emotional State

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