EEG based pattern recognition method for classification of different mental tasking: Preliminary study for stroke survivors in Indonesia

Caesarendra, Wahyu; Ariyanto, Mochammad; Lexon, Syahara U.; Pasmanasari, Elta Diah; Chang, Cindy R.; Setiawan, Joga Dharma

doi:10.1109/icacomit.2015.7440193

Cited by 7 publications

(2 citation statements)

References 19 publications

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“…Suhas, Dhal (40) investigated using ERPs to control a light bulb and a fan, with an eye towards giving physically disabled individuals control of 'smart' appliances. Other studies have employed EPOC as a means of controlling robots[41][42][43][44][45][46][47], tractors[48], and drones[49].Practical and effective BCI device control using EEG has the potential to benefit a large population, such as individuals who have lost the use of motor functions. For this reason, this area of research has received much attention and it should be expected to continue to do so.…”

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confidence: 99%

10 years of EPOC: A scoping review of Emotiv’s portable EEG device

Williams

McArthur

Badcock

2020

Preprint

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BACKGROUND Commercially-made low-cost electroencephalography (EEG) devices have become increasingly available over the last decade. One of these devices, Emotiv EPOC, is currently used in a wide variety of settings, including brain-computer interface (BCI) and cognitive neuroscience research. PURPOSE The aim of this study was to chart peer-reviewed reports of Emotiv EPOC projects to provide an informed summary on the use of this device for scientific purposes. METHODS We followed a five-stage methodological framework for a scoping review that included a systematic search using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines. We searched the following electronic databases: PsychINFO, MEDLINE, Embase, Web of Science, and IEEE Xplore. We charted study data according to application (BCI, clinical, signal processing, experimental research, and validation) and location of use (as indexed by the first author’s address). RESULTS We identified 382 relevant studies. The top five publishing countries were the United States (n = 35), India (n = 25), China (n = 20), Poland (n = 17), and Pakistan (n = 17). The top five publishing cities were Islamabad (n = 11), Singapore (n = 10), Cairo, Sydney, and Bandung (n = 7 each). Most of these studies used Emotiv EPOC for BCI purposes (n = 277), followed by experimental research (n = 51). Thirty-one studies were aimed at validating EPOC as an EEG device and a handful of studies used EPOC for improving EEG signal processing (n = 12) or for clinical purposes (n = 11). CONCLUSIONS In its first 10 years, Emotiv EPOC has been used around the world in diverse applications, from control of robotic limbs and wheelchairs to user authentication in security systems to identification of emotional states. Given the widespread use and breadth of applications, it is clear that researchers are embracing this technology.

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confidence: 99%

10 years of EPOC: A scoping review of Emotiv’s portable EEG device

Williams

McArthur

Badcock

2020

Preprint

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“…Features are play an important role in classification accuracy. The previous study investigated 18 features which were used in ANN classifier (Caesarendra et al 2015). The result shows that the better accuracy are channel F7 and F8 with accuracy of 80% and 85%, respectively.…”

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confidence: 99%

Mental Tasks EEG Signal Classification Using Support Vector Machine

Caesarendra

Lekson

Agung

2019

Journal of Infrastructure &Amp; Facility Asset Management

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This paper presents a result of electroencephalography (EEG) signal classification for mental tasks such as thinking forward, backward, left, and right. The EEG data in this study were recorded from Emotive device with 14 channels and 2 references. The aim of this study is to identify the most sensitive channels to the mental task classification. Prior to feature extraction, the EEG signal were decomposed using wavelet with three level decomposition. Eighteen features were extracted from the processed data. Principal component analysis (PCA) is then used to reduce 18 features into 3 principal components. The principal component were classified using support vector machine (SVM). The results show that the SVM classification accuracy of 75%.

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