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2020 | OriginalPaper | Chapter

Investigating the Possibility of Brain Actuated Mobile Robot Through Single-Channel EEG Headset

Authors : Mamunur Rashid, Norizam Sulaiman, Mahfuzah Mustafa, Sabira Khatun, Bifta Sama Bari, Md Jahid Hasan, Nawfan M. M. A. Al-Fakih

Published in: InECCE2019

Publisher: Springer Singapore

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Abstract

Brain-computer interface (BCI) is a fast-growing technology involving hardware and software communication systems that allow controlling external assistive devices through Electroencephalogram (EEG). The primary goal of BCI technology is to ensure a potential communication pathway for patients with severe neurologic disabilities. A variety of BCI applications have been presented in the last few decades which indicate that the interest in this field has dramatically increased. In this paper, the possibility of a brain-actuated mobile robot using single-channel EEG headset has been investigated. EEG data has been collected from Neurosky Mindwave EEG headset which consists of a single electrode. EEG feature in terms of power spectral density (PSD) has been extracted and classified this feature using the support vector machine (SVM). Then the classified signal has been translated into three devices command to control the mobile robot. This mobile robot can be driven in three directions namely forward, right and left direction. Data collection from EEG headset and sending commands to a mobile robot, the entire process has been done wirelessly.

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Literature
1.
Bi L, Fan X-A, Liu Y (2013) EEG-based brain-controlled mobile robots: a survey. IEEE Trans. Human-Machine Syst. 43:161–176CrossRef
2.
Stephygraph LR, Arunkumar N (2016) Brain-actuated wireless mobile robot control through an adaptive human–machine interface. In: Suresh L, Panigrahi B (eds), Proceedings of the international conference on soft computing systems (Advances in intelligent systems and computing), vol 397. Springer, New Delhi
3.
Ruşanu OA, Cristea L, Luculescu MC, Cotfas PA (2018) A brain-computer interface based on the integration of NI myRIO development device and NeuroSky mindwave headset. IOP Conf Ser Mater Sci Eng 444
4.
Prathibha R, Swetha L, Shobha KR (2017) Brain computer interface: design and development of a smart robotic gripper for a prosthesis environment. In: 2017 International conference on networks & advances in computational technologies (NetACT), pp 278–283
5.
Patel K, Shah H, Dcosta M, Shastri D (2017) Evaluating NeuroSky’s single-channel EEG sensor for drowsiness detection. Presented at the (2017)
6.
Kan DPX, Lim VWW, Lee PF (2015) Signal conversion from attention signals to light emitting diodes as an attention level indicator. In: Su FC, Wang SH, Yeh ML (eds) IFMBE proceedings of the 1st global conference on biomedical engineering & 9th Asian-Pacific conference on medical and biological engineering, vol 47. Springer, Cham
7.
Bright D, Nair A, Salvekar D, Bhisikar S (2016) EEG-based brain controlled prosthetic arm. In: Conference on advances in signal processing, CASP 2016, pp 479–483 (2016)
8.
Rashid M, Sulaiman N, Mustafa M, Khatun S, Bari BS (2019) The classification of EEG signal using different machine learning techniques for BCI application. In: Kim J-H, Myung H, Lee S-M (eds) Robot intelligence technology and applications. RiTA 2018. communications in computer and information science, vol 1015. Springer, Singapore, pp 207–221
9.
Biswas T, Ahmad Fauzi MF, Abas FS, Nair HKR (2018) Superpixel classification with color and texture features for automated wound area segmentation. 2018 IEEE Student Conference on Research and Development (SCOReD), pp 1–6
10.
11.
Atkinson J, Campos D, John Atkinson DC (2016) Improving BCI-based emotion recognition by combining EEG feature selection and kernel classifiers. Expert Syst. Appl. 47:35–41CrossRef
12.
Otsuka T, Watanabe K, Hirano Y, Kubo K, Miyake S, Sato S, Sasaguri K (2009) Effects of mandibular deviation on brain activation during clenching: an fMRI preliminary study. Cranio J. Craniomandib. Pract. 27:88–93
13.
Liu Y, Yu M, Zhao G, Song J, Ge Y, Shi Y (2017) Real-time movie-induced discrete emotion recognition from EEG signals. IEEE Trans Affect Comput, 1
14.
Bastos-Filho TF, Ferreira A, Atencio AC, Arjunan S, Kumar D (2012) Evaluation of feature extraction techniques in emotional state recognition. In: 4th International conference on intelligent human computer interaction (IHCI 2012)
15.
Srinivas V, Rama V, Rao CBR (2016) Wavelet based emotion recognition using RBF algorithm. Int J Innov Res Electr Electron Instrum Control Eng 4:29–34
16.
Pan J, Li Y, Wang J (2016) An EEG-based brain-computer interface for emotion recognition. In: Proceedings of the international joint conference on neural networks
17.
Lokannavar S, Lahane P, Gangurde A, Chidre P (2015) Emotion recognition using EEG signals. Int J Adv Res Comput Commun Eng 4:118–127
18.
Jatupaiboon N, Pan-Ngum S, Israsena P (2013) Emotion classification using minimal EEG channels and frequency bands. In: Proceedings of 2013 10th international joint conference on computer science and software engineering (JCSSE 2013), pp 21–24
19.
Murugappan M (2011) Human emotion classification using wavelet transform and KNN. In: 2011 international Conference on pattern analysis and intelligence robotics. IEEE, pp 148–153
20.
Murugappan M, Nagarajan R, Yaacob S (2011) Combining spatial filtering and wavelet transform for classifying human emotions using EEG signals. J Med Biol Eng 31:45–51CrossRef
21.
Chanel G, Kierkels JJM, Soleymani M, Pun T (2009) Short-term emotion assessment in a recall paradigm. Int J Hum Comput Stud 67:607–627
Metadata
Title
Investigating the Possibility of Brain Actuated Mobile Robot Through Single-Channel EEG Headset
Authors
Mamunur Rashid
Norizam Sulaiman
Mahfuzah Mustafa
Sabira Khatun
Bifta Sama Bari
Md Jahid Hasan
Nawfan M. M. A. Al-Fakih
Copyright Year
2020
Publisher
Springer Singapore
Book
InECCE2019

Print ISBN: 978-981-15-2316-8

Electronic ISBN: 978-981-15-2317-5

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-981-15-2317-5_49