Classification of primitive shapes using brain–computer interfaces

Esfahani, Ehsan T.; Sundararajan, V.

doi:10.1016/j.cad.2011.04.008

Cited by 59 publications

(47 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Researchers have investigated different EEG processing algorithms to assess classification of shapes being thought about [45], detection of hand movement intentions on the same side of the brain as the hand [46], classification of positive and negative emotion elicited by pictures [47][48] [49], and evaluation of cognitive workload [50].…”

Section: Eeg To Isolate Specific Game Eventsmentioning

confidence: 99%

Modality specific assessment of video game player’s experience using the Emotiv

McMahan

Parberry

Parsons

2015

Entertainment Computing

View full text Add to dashboard Cite

Section: Eeg To Isolate Specific Game Eventsmentioning

confidence: 99%

Modality specific assessment of video game player’s experience using the Emotiv

McMahan

Parberry

Parsons

2015

Entertainment Computing

View full text Add to dashboard Cite

“…The Emotiv EPOC headset represents a practical, economical, and efficient alternative for EEG-based applications, which has been recently used in a number of applications such as emotions detection supporting instant messaging [12], visual imagery for classification of primitive shapes [13], P300 rhythm detection [14], or a human-machine interface applied into a tractor steering [15]. In the project described in this paper, the Emotiv headset is used to emulate the basic operations of a mouse computer, with position control based on the speed signals obtained from a gyroscope included in the headset.…”

Section: Introductionmentioning

confidence: 99%

Gyroscope-Driven Mouse Pointer with an EMOTIV® EEG Headset and Data Analysis Based on Empirical Mode Decomposition

Rosas-Cholula

Ramírez-Cortés

Alarcón-Aquino

et al. 2013

Sensors

View full text Add to dashboard Cite

This paper presents a project on the development of a cursor control emulating the typical operations of a computer-mouse, using gyroscope and eye-blinking electromyographic signals which are obtained through a commercial 16-electrode wireless headset, recently released by Emotiv. The cursor position is controlled using information from a gyroscope included in the headset. The clicks are generated through the user's blinking with an adequate detection procedure based on the spectral-like technique called Empirical Mode Decomposition (EMD). EMD is proposed as a simple and quick computational tool, yet effective, aimed to artifact reduction from head movements as well as a method to detect blinking signals for mouse control. Kalman filter is used as state estimator for mouse position control and jitter removal. The detection rate obtained in average was 94.9%. Experimental setup and some obtained results are presented.

show abstract

“…And supporting arguments have been given by a number of researchers who have successfully applied this system to assess cognitive processes [12][13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 88%

Auditory evoked potentials in patients with major depressive disorder measured by Emotiv system

Wang

Yang-de

et al. 2015

BME

View full text Add to dashboard Cite

Abstract. In a previous study (unpublished), Emotiv headset was validated for capturing event-related potentials (ERPs) from normal subjects. In the present follow-up study, the signal quality of Emotiv headset was tested by the accuracy rate of discriminating Major Depressive Disorder (MDD) patients from the normal subjects. ERPs of 22 MDD patients and 15 normal subjects were induced by an auditory oddball task and the amplitude of N1, N2 and P3 of ERP components were specifically analyzed. The features of ERPs were statistically investigated. It is found that Emotiv headset is capable of discriminating the abnormal N1, N2 and P3 components in MDD patients. Relief-F algorithm was applied to all features for feature selection. The selected features were then input to a linear discriminant analysis (LDA) classifier with leave-one-out cross-validation to characterize the ERP features of MDD. 127 possible combinations out of the selected 7 ERP features were classified using LDA. The best classification accuracy was achieved to be 89.66%. These results suggest that MDD patients are identifiable from normal subjects by ERPs measured by Emotiv headset.

show abstract

Classification of primitive shapes using brain–computer interfaces

Cited by 59 publications

References 36 publications

Modality specific assessment of video game player’s experience using the Emotiv

Modality specific assessment of video game player’s experience using the Emotiv

Gyroscope-Driven Mouse Pointer with an EMOTIV® EEG Headset and Data Analysis Based on Empirical Mode Decomposition

Auditory evoked potentials in patients with major depressive disorder measured by Emotiv system

Contact Info

Product

Resources

About