Evolutionary perspective for optimal selection of EEG electrodes and features

Lahiri, Rimita; Rakshit, Pratyusha; Konar, Amit

doi:10.1016/j.bspc.2017.03.022

Cited by 19 publications

(11 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The literature describes that similar or even the same performance could be accomplished by using a more compact group of channels [ 23 , 42 ]. However, researchers still argue about the number and locations of the EEG electrodes [ 17 , 20 , 43 ]. Therefore, more comprehensive studies on channel selection are of great importance.…”

Section: Related Workmentioning

confidence: 99%

“…Two approaches can be distinguished within the undertaken research works: optimal selection of electrodes [ 17 ], optimal extraction and selection of features [ 18 ]. …”

Section: Introductionmentioning

confidence: 99%

“…However, due to the complexity of the electrode selection and the need to adapt the research to the classification problem, the attempts made so far have not clearly indicated the number and the position of electrodes best suited to the tasks related to emotions’ classification. For example, the study [ 20 ] suggests using nine electrodes, while the authors of the research [ 17 ] indicate three to eight electrodes depending on the experiment. An additional difficulty is that the smaller sets of electrodes do not constitute subsets of the covering sets of electrodes in the cited works.…”

Section: Introductionmentioning

confidence: 99%

“…An additional difficulty is that the smaller sets of electrodes do not constitute subsets of the covering sets of electrodes in the cited works. The selected electrodes proposed in studies [ 20 , 21 ] are located over the frontal and parietal lobes, whereas [ 17 ] indicates mainly central and occipital lobes.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Hybrid Method of Automated EEG Signals’ Selection Using Reversed Correlation Algorithm for Improved Classification of Emotions

Wosiak

Dura

2020

Sensors

View full text Add to dashboard Cite

Based on the growing interest in encephalography to enhance human–computer interaction (HCI) and develop brain–computer interfaces (BCIs) for control and monitoring applications, efficient information retrieval from EEG sensors is of great importance. It is difficult due to noise from the internal and external artifacts and physiological interferences. The enhancement of the EEG-based emotion recognition processes can be achieved by selecting features that should be taken into account in further analysis. Therefore, the automatic feature selection of EEG signals is an important research area. We propose a multistep hybrid approach incorporating the Reversed Correlation Algorithm for automated frequency band—electrode combinations selection. Our method is simple to use and significantly reduces the number of sensors to only three channels. The proposed method has been verified by experiments performed on the DEAP dataset. The obtained effects have been evaluated regarding the accuracy of two emotions—valence and arousal. In comparison to other research studies, our method achieved classification results that were 4.20–8.44% greater. Moreover, it can be perceived as a universal EEG signal classification technique, as it belongs to unsupervised methods.

show abstract

Section: Related Workmentioning

confidence: 99%

“…Two approaches can be distinguished within the undertaken research works: optimal selection of electrodes [ 17 ], optimal extraction and selection of features [ 18 ]. …”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Hybrid Method of Automated EEG Signals’ Selection Using Reversed Correlation Algorithm for Improved Classification of Emotions

Wosiak

Dura

2020

Sensors

View full text Add to dashboard Cite

show abstract

“…Similarly to the specific problems that EMG sensors have, multi-channel EEG sensors will reduce the robustness of the system. Moreover, since the human brain’s structure is directly related to the signal characteristics [52], it is important to select an appropriate position in the helmet to detect fatigue in soldiers. With the development of Augmented Reality technology, smart glasses integrated into helmets will provide soldiers with more information on the battlefield.…”

Section: Systematic Analysis Of Framework Unitsmentioning

confidence: 99%

Systematic Analysis of a Military Wearable Device Based on a Multi-Level Fusion Framework: Research Directions

Shi

Zhang

Liu

et al. 2019

Sensors

View full text Add to dashboard Cite

With the development of the Internet of Battlefield Things (IoBT), soldiers have become key nodes of information collection and resource control on the battlefield. It has become a trend to develop wearable devices with diverse functions for the military. However, although densely deployed wearable sensors provide a platform for comprehensively monitoring the status of soldiers, wearable technology based on multi-source fusion lacks a generalized research system to highlight the advantages of heterogeneous sensor networks and information fusion. Therefore, this paper proposes a multi-level fusion framework (MLFF) based on Body Sensor Networks (BSNs) of soldiers, and describes a model of the deployment of heterogeneous sensor networks. The proposed framework covers multiple types of information at a single node, including behaviors, physiology, emotions, fatigue, environments, and locations, so as to enable Soldier-BSNs to obtain sufficient evidence, decision-making ability, and information resilience under resource constraints. In addition, we systematically discuss the problems and solutions of each unit according to the frame structure to identify research directions for the development of wearable devices for the military.

show abstract

Reversed Correlation-Based Pairwised EEG Channel Selection in Emotional State Recognition

Dura

Wosiak

Stasiak

et al. 2021

Computational Science – ICCS 2021

View full text Add to dashboard Cite

Evolutionary perspective for optimal selection of EEG electrodes and features

Cited by 19 publications

References 50 publications

Hybrid Method of Automated EEG Signals’ Selection Using Reversed Correlation Algorithm for Improved Classification of Emotions

Hybrid Method of Automated EEG Signals’ Selection Using Reversed Correlation Algorithm for Improved Classification of Emotions

Systematic Analysis of a Military Wearable Device Based on a Multi-Level Fusion Framework: Research Directions

Reversed Correlation-Based Pairwised EEG Channel Selection in Emotional State Recognition

Contact Info

Product

Resources

About