Classification of left/right hand movement from EEG signal by intelligent algorithms

Baig, Muhammad Zeeshan; Javed, Ehtasham; Ayaz, Yasar; Afzal, Waseem; Gillani, Syed Omer; Muhammad, Naveed; Jamil, Mohsin

doi:10.1109/iscaie.2014.7010230

Cited by 11 publications

(6 citation statements)

References 11 publications

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“…The EEG is the most widely used to measure brain activity because its electrodes are non-invasive and portable. A full EEG headset comprises more than 128 channels; however, some experiments use fewer electrodes in neuro-feedback practice [55]. Experimental studies have shown that the EEG has the potential to differentiate positive emotional valence from negative emotional valence.…”

Section: Electroencephalographymentioning

confidence: 99%

A Survey on Psycho-Physiological Analysis & Measurement Methods in Multimodal Systems

Baig¹,

Kavakli²

2019

MTI

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Psycho-physiological analysis has gained greater attention in the last few decades in various fields including multimodal systems. Researchers use psychophysiological feedback devices such as skin conductance (SC), Electroencephalography (EEG) and Electrocardiography (ECG) to detect the affective states of the users during task performance. Psycho-physiological feedback has been successful in detection of the cognitive states of users in human-computer interaction (HCI). Recently, in game studies, psycho-physiological feedback has been used to capture the user experience and the effect of interaction on human psychology. This paper reviews several psycho-physiological, cognitive, and affective assessment studies and focuses on the use of psychophysiological signals in estimating the user's cognitive and emotional states in multimodal systems. In this paper, we review the measurement techniques and methods that have been used to record psycho-physiological signals as well as the cognitive and emotional states in a variety of conditions. The aim of this review is to conduct a detailed study to identify, describe and analyze the key psycho-physiological parameters that relate to different mental and emotional states in order to provide an insight into key approaches. Furthermore, the advantages and limitations of these approaches are also highlighted in this paper. The findings state that the classification accuracy of >90% has been achieved in classifying emotions with EEG signals. A strong correlation between self-reported data, HCI experience, and psychophysiological data has been observed in a wide range of domains including games, human-robot interaction, mobile interaction, and simulations. An increase in β and γ-band activity have been observed in high intense games and simulations.

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Section: Electroencephalographymentioning

confidence: 99%

A Survey on Psycho-Physiological Analysis & Measurement Methods in Multimodal Systems

Baig¹,

Kavakli²

2019

MTI

Self Cite

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“…EEG is a common tool to measure brain activity as it is inexpensive, non-invasive, and has high temporal resolution. EEG has been used in many applications ranging from medical diagnostics, cognition, to brain–computer interfaces [19,20,21]. In this paper, FBNs were constructed from a non-linear connectivity measure known as NTE.…”

Section: Methodsmentioning

confidence: 99%

Connectivity Analysis Using Functional Brain Networks to Evaluate Cognitive Activity during 3D Modelling

Baig

Kavakli

2019

Brain Sciences

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Modelling 3D objects in CAD software requires special skills which require a novice user to undergo a series of training exercises to obtain. To minimize the training time for a novice user, the user-dependent factors must be studied. we have presented a comparative analysis of novice/expert information flow patterns. We have used Normalized Transfer Entropy (NTE) and Electroencephalogram (EEG) to investigate the differences. The experiment was divided into three cognitive states i.e., rest, drawing, and manipulation. We applied classification algorithms on NTE matrices and graph theory measures to see the effectiveness of NTE. The results revealed that the experts show approximately the same cognitive activation in drawing and manipulation states, whereas for novices the brain activation is more in manipulation state than drawing state. The hemisphere- and lobe-wise analysis showed that expert users have developed an ability to control the information flow in various brain regions. On the other hand, novice users have shown a continuous increase in information flow activity in almost all regions when doing drawing and manipulation tasks. A classification accuracy of more than 90% was achieved with a simple K-nearest neighbors (k-NN) to classify novice and expert users. The results showed that the proposed technique can be used to develop adaptive 3D modelling systems.

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“…Fourier and Wavelet transform [98,99] were also used in the literature to extract features and these features lie under transform features category. For frequency domain features, power spectral density and spectral edge frequency-based features were mostly used in the literature [100,101]. After reviewing the literature for channel selection algorithms, the most effective features are the ones extracted by CSP and its variations.…”

Section: Feature Extractionmentioning

confidence: 99%

Filtering techniques for channel selection in motor imagery EEG applications: a survey

2019

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Brain Computer Interface (BCI) Systems are used in a wide range of applications such as communication, neuro-prosthetic and environmental control for disabled persons using robots and manipulators. A typical BCI system uses different types of inputs; however, Electroencephalography (EEG) signals are most widely used due to their non-invasive EEG electrodes, portability, and cost efficiency. The signals generated by the brain while performing or imagining a motor related task (Motor Imagery (MI)) signals are one of the important inputs for BCI applications. EEG data is usually recorded from more than 100 locations across the brain, so efficient channel selection algorithms are of great importance to identify optimal channels related to a particular application. The main purpose of applying channel selection is to reduce computational complexity while analysing EEG signals, improve classification accuracy by reducing over-fitting, and decrease setup time. Different channel selection evaluation algorithms such as filtering, wrapper, and hybrid methods have been used for extracting optimal channel subsets by using predefined criteria. After extensively reviewing the literature in the field of EEG channel

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Classification of left/right hand movement from EEG signal by intelligent algorithms

Cited by 11 publications

References 11 publications

A Survey on Psycho-Physiological Analysis & Measurement Methods in Multimodal Systems

A Survey on Psycho-Physiological Analysis & Measurement Methods in Multimodal Systems

Connectivity Analysis Using Functional Brain Networks to Evaluate Cognitive Activity during 3D Modelling

Filtering techniques for channel selection in motor imagery EEG applications: a survey

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