EEG feature selection for thought driven robots using evolutionary Algorithms

Amarasinghe, Kasun; Sivils, Patrick; Manic, Milos

doi:10.1109/hsi.2016.7529657

Cited by 13 publications

(9 citation statements)

References 26 publications

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“…From a brief literature review, we confirmed that GA variations are widely used and employ a binary coding for the EEG feature vector (Ko lodziej et al, 2011;Amarasinghe et al, 2016;Cîmpanu et al, 2017;Wen & Zhang, 2017;Leon et al, 2019;Moctezuma & Molinas, 2020). The authors also prefer the usage of supervised learning techniques, e.g., Linear Discriminant Analysis (LDA) (Ko lodziej et al, 2011) and Support Vector Machines (SVMs) (Rejer & Lorenz, 2013;Amarasinghe et al, 2016;Cîmpanu et al, 2017;Leon et al, 2019), and external measures to evaluate both fitness functions and models.…”

Section: Introductionmentioning

confidence: 68%

“…Especially, one of the most employed evolutionary techniques among the ones proposed by different authors, is the genetic algorithm (GA) (Goldberg & Holland, 1988) with its variations (Rejer & Lorenz, 2013;Amarasinghe et al, 2016;Wen & Zhang, 2017;Rejer & Twardochleb, 2018;Shon et al, 2018;Leon et al, 2019;Moctezuma & Molinas, 2020), due to the fact that it has many appealing characteristics. Apart from being able to perform the feature selection without relying on expert knowledge and thus avoiding the introduction of assumptions on the features, as other evolutionary algorithms (Xue et al, 2015), it is robust and efficient (Shon et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

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Genetic algorithm for feature selection of EEG heterogeneous data

Saibene,

Gasparini

2021

Preprint

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The electroencephalographic (EEG) signals provide highly informative data on brain activities and functions. However, their heterogeneity and high dimensionality may represent an obstacle for their interpretation. The introduction of a priori knowledge seems the best option to mitigate high dimensionality problems, but could lose some information and patterns present in the data, while data heterogeneity remains an open issue that often makes generalization difficult. In this study, we propose a genetic algorithm (GA) for feature selection that can be used with a supervised or unsupervised approach. Our proposal considers three different fitness functions without relying on expert knowledge.Starting from two publicly available datasets on cognitive workload and motor movement/imagery, the EEG signals are processed, normalized and their features computed in the time, frequency and time-frequency domains. The feature vector selection is performed by applying our GA proposal and compared with two benchmarking techniques. The results show that different combinations of our proposal achieve better results in respect to the benchmark in terms of overall performance and feature reduction. Moreover, the proposed GA, based on a novel fitness function here presented, outperforms the benchmark when the two different datasets considered are merged together, showing the effectiveness of our proposal on heterogeneous data.

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Section: Introductionmentioning

confidence: 68%

Section: Introductionmentioning

confidence: 99%

Genetic algorithm for feature selection of EEG heterogeneous data

Saibene,

Gasparini

2021

Preprint

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“…With three features and 14 channels, they obtained an average accuracy of 97.34%. In the article by Kasun Amarasinghe [46], where the sensing headset collected the data, the feature selection method was used in addition to the SVM, ANN, and NB classifiers. With 14 channels and 11 features selected for three classes and one person for NB, the accuracy was 82.97% for ANN 83.07% and 83.26% for SVM.…”

Section: Previous Workmentioning

confidence: 99%

Dimension Reduction Using New Bond Graph Algorithm and Deep Learning Pooling on EEG Signals for BCI

et al. 2021

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One of the main challenges in studying brain signals is the large size of the data due to the use of many electrodes and the time-consuming sampling. Choosing the right dimensional reduction method can lead to a reduction in the data processing time. Evolutionary algorithms are one of the methods used to reduce the dimensions in the field of EEG brain signals, which have shown better performance than other common methods. In this article, (1) a new Bond Graph algorithm (BGA) is introduced that has demonstrated better performance on eight benchmark functions compared to genetic algorithm and particle swarm optimization. Our algorithm has fast convergence and does not get stuck in local optimums. (2) Reductions of features, electrodes, and the frequency range have been evaluated simultaneously for brain signals (left-handed and right-handed). BGA and other algorithms are used to reduce features. (3) Feature extraction and feature selection (with algorithms) for time domain, frequency domain, wavelet coefficients, and autoregression have been studied as well as electrode reduction and frequency interval reduction. (4) First, the features/properties (algorithms) are reduced, the electrodes are reduced, and the frequency range is reduced, which is followed by the construction of new signals based on the proposed formulas. Then, a Common Spatial Pattern is used to remove noise and feature extraction and is classified by a classifier. (5) A separate study with a deep sampling method has been implemented as feature selection in several layers with functions and different window sizes. This part is also associated with reducing the feature and reducing the frequency range. All items expressed in data set IIa from BCI competition IV (the left hand and right hand) have been evaluated between one and three channels, with better results for similar cases (in close proximity). Our method demonstrated an increased accuracy by 5 to 8% and an increased kappa by 5%.

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“…where X is input time series signal and N is size of the signal. -FV [3]: This feature corresponds to the variance of the raw signal calculated using (4), where X is input time series signal and N is size of the signal.…”

Section: Feature Extractionmentioning

confidence: 99%

“…Electroencephalogram (EEG) has been widely used by researchers to capture the brainwave signals for determining the emotional state of the user [29]. EEG based devices have also been used for Brain-Computer-Interfaces (BCI) like cursor control and thought driven robots [3]. They have also been applied to serve as a user identification method in [20,24].…”

Section: Introductionmentioning

confidence: 99%

Enhancing Text Using Emotion Detected from EEG Signals

et al. 2018

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Often people might not be able to express themselves properly on social media, like not being able to think of appropriate words representative of their emotional state. In this paper, we propose an end to end system which aims to enhance user-input sentence according to his/her current emotional state. It works by a) detecting the emotion of the user and b) enhancing the input sentence by inserting emotive words to make the sentence more representative of the emotional state of the user. The emotional state of the user is recognized by analyzing the Electroencephalogram (EEG) signals from the brain. For text enhancement, we modify the words corresponding to the detected emotion using correlation finder scheme. Next, the verification of the sentence correctness has been performed using Long Short Term Memory (LSTM) Networks based Language Modeling framework. An accuracy of 74.95% has been recorded for the classification of five emotional states in a dataset of 25 participants using EEG signals. Similarly, promising results have been obtained for the task text enhancement and overall end-to-end system. To the best of our knowledge, this work is the first of its kind to enhance text according to the emotional state detected by EEG brainwaves. The system also releases an individual from thinking and typing words, which might be a complicated procedure sometimes.

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EEG feature selection for thought driven robots using evolutionary Algorithms

Cited by 13 publications

References 26 publications

Genetic algorithm for feature selection of EEG heterogeneous data

Genetic algorithm for feature selection of EEG heterogeneous data

Dimension Reduction Using New Bond Graph Algorithm and Deep Learning Pooling on EEG Signals for BCI

Enhancing Text Using Emotion Detected from EEG Signals

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