Channel selection from EEG signals and application of support vector machine on EEG data

Arslan, Mustafa Turan; Eraldemir, Server Göksel; Yıldırım, Esen

doi:10.1109/idap.2017.8090226

Cited by 6 publications

(1 citation statement)

References 16 publications

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“…The SVM uses core functions to create a plane that can carry datasets, which are difficult to separate linearly into a high-dimensional space and maximizes the space between classes. Scientific studies reveal that the SVM contributes to better classification accuracy than other algorithms with feature vectors obtained from EEG signals [ 57 , 58 ]. A Gaussian radial basis function (RBF) kernel was used to enhance the data separability [ 55 , 59 , 60 ].…”

Section: Methodsmentioning

confidence: 99%

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

Wosiak

Dura

2020

Sensors

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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.

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Section: Methodsmentioning

confidence: 99%

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

Wosiak

Dura

2020

Sensors

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Music’s effect on the human brain: A survey on music-induced brainwave analysis

Suryawanshi,

Badgujar,

Das

et al. 2023

RECENT ADVANCES IN SCIENCES, ENGINEERING, INFORMATION TECHNOLOGY &Amp; MANAGEMENT

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EEG-EMG based bio-robotics elbow orthotics control

Ferdiansyah

Prajitno²,

Wijaya³

2020

J. Phys.: Conf. Ser.

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Brain-computer interface (BCI) or also its advancement, hybrid brain-computer interface (hBCI), is a technology that is vastly developed. This technology has been used in many fields. BCI is a system that directly changes a human’s mind into data that can be extracted to information that can be meaningful to people. The development of this technology has applications as a rehabilitation aid for someone suffering from an inability to move his limbs, such as the arms. Through this research, it is hoped to be able to design an orthosis control system as a rehabilitation device by using a classification method with EEG and EMG signals, so that subjects who use this tool can carry out rehabilitation in upper arm movements especially in the elbow joint. The system utilized Raspberry Pi 3 B+ as the computer and ADS1299EEG-FE as analog front end for EEG and EMG. EEG frequency band power and EMG Vrms feature are extracted using Wavelet Transform and the model used for movement classification is Support Vector Machine. The results of the movement classification using both signals, using delta alpha ratio and root mean square features, obtained training accuracy for three movements namely relax, flexion, and extension of 90.3% and for testing accuracy of 85.2%. The combination of EEG and EMG signals are considered a promising approach for developing rehabilitation device of right arm limb.

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Channel selection from EEG signals and application of support vector machine on EEG data

Cited by 6 publications

References 16 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

Music’s effect on the human brain: A survey on music-induced brainwave analysis

EEG-EMG based bio-robotics elbow orthotics control

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