Smart Room System for Paralysis Patients with Mindwave EEG Sensor Control

Arrya Anandika - Universitas Andalas, Limau Manis Street, Padang City, 21653, Indonesia
Rian Ferdian - Universitas Andalas, Limau Manis Street, Padang City, 21653, Indonesia
Alivia Eriyandha - Universitas Andalas, Limau Manis Street, Padang City, 21653, Indonesia
Rifki Suwandi - Universitas Andalas, Limau Manis Street, Padang City, 21653, Indonesia
Muhammad Hafidz - Universitas Andalas, Limau Manis Street, Padang City, 21653, Indonesia

Citation Format:



Persons with disabilities experience physical, intellectual, mental, or sensory difficulties. One type of disability is paralysis. Paralysis is a condition where there is interference with the nerves that control body movement, causing the limbs to be unable to move. Paralyzed people will find it difficult to move without the help of others. Therefore, research was carried out by creating an intelligent room system to help persons with disabilities manage their own rooms so that they do not always have to be accompanied by a nurse. Paralyzed people can turn lights or fans on and off, and send help messages to their carers via the Telegram bot. This study used the NeuroSky Mindwave EEG headset which detects the user's brain signals with outputs in the form of attention level, relaxation level (meditation), and blink strength level. The resulting signal is processed via a PC and sent via NodeMCU to give commands in the form of turning lights and fans on or off, as well as sending messages to nurses. From this research a system was produced that could turn on the lights based on the value of Attention ≥ 70, turn on the fan based on the Meditation value ≥ 74, then the value of BlinkStrength ≥ 81 which was counted 2 times to turn off the lights, 3 times to turn off the fan, 4 times to turn off the lights and fan, and more than 4 times sending help messages


Paralysis; Smart Room; NeuroSky Mindwave; Attention; Meditation; BlinkStrength

Full Text:



V. Yulaswati, F. Nursyamsi, M. N. Ramadhan, H. Palani, and E. K. Yazid, “PENYANDANG DISABILITAS INDONESIA : ASPEK SOSIOEKONOMI DAN YURIDIS,” Jakarta, 2021.

A. Al Bakri, M. Y. Lezzar, M. Alzinati, K. Mortazavi, W. Shehieb, and T. Sharif, “Intelligent Exoskeleton for Patients with Paralysis,” in 2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON) , 2018, pp. 189–193.

M. I. Rusydi, Oktrison, W. Azhar, S. W. Oluwarotimi, and F. Rusydi, “Towards hand gesture-based control of virtual keyboards for effective communication,” in IOP Conference Series: Materials Science and Engineering, Institute of Physics Publishing, Sep. 2019. doi: 10.1088/1757-899X/602/1/012030.

Z. Pirani, A. Momin, A. Kadri, and A. Shaikh, “Survey of Numerous Accessible Applications for Paralysis Patients,” Proceedings of the Second International Conference on Intelligent Computing and Control Systems (ICICCS), pp. 1966–1969, 2018.

D. Gaikar, P. Porlekar, D. Shetty, A. Shitkar, P. Kalebere, and A. Professor, “AUTOMATED PARALYSIS PATIENT HEALTHCARE SYSTEM,” International Journal of Creative Research Thoughts (IJCRT), vol. 9, no. 8, pp. 251–256, 2021, [Online]. Available:

E. Lopez-Larraz, N. Birbaumer, and A. Ramos-Murguialday, “A hybrid EEG-EMG BMI improves the detection of movementintention in cortical stroke patients with complete hand paralysis,” in 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society : Learning from the Past, Looking to the Future, 2018, pp. 2000–2003.

N. Naseeb, M. Alam, O. Bin Samin, M. Omar, S. S. Khushbakht, and S. A. Shah, “RGB based EEG Controlled Virtual Keyboard forPhysically Challenged People,” in 3rd International Conference on Computing, Mathematics and Engineering Technologies : iCoMET : Idea to Innovation for Building the Knowledge Economy, 2020.

R. Avudaiammal, K. J. Mystica, A. Balaji, and B. Raja, “Brain sense controlled wireless robot: Interfacing neurosky brainsense to a wheelchair prototype,” in Proceedings of the 3rd International Conference on Smart Systems and Inventive Technology, ICSSIT 2020, Institute of Electrical and Electronics Engineers Inc., Aug. 2020, pp. 276–280. doi: 10.1109/ICSSIT48917.2020.9214100.

S. H. Shehab, M. L. Rahman, M. H. Hasan, M. I. Uddin, S. A. Mahmood, and A. E. Chowdhury, “Home automation system using gesture pattern voice recognition for paralyzed people,” in Proceedings of 2020 11th International Conference on Electrical and Computer Engineering, ICECE 2020, Institute of Electrical and Electronics Engineers Inc., Dec. 2020, pp. 25–28. doi: 10.1109/ICECE51571.2020.9393142.

M. I. Rusydi et al., “The Use of Two Fingers to Control Virtual Keyboards with Leap Motion Sensor,” 2017 5th International Conference on Instrumentation, Communications, Information Technology, and Biomedical Engineering (ICICI-BME), pp. 255–260, 2017.

M. I. Rusydi, A. Anandika, R. Adnan, K. Matsuhita, and M. Sasaki, “Adaptive Symmetrical Virtual Keyboard Based on EOG Signal,” pp. 22–26, 2019.

M. I. Rusydi, D. Saputra, D. Anugrah, S. Syafii, A. W. Setiawan, and M. Sasaki, “Real Time Control of Virtual Menu Based on EMG Signal from Jaw,” 3rd Asia-Pacific Conference on Intelligent Robot Systems : ACIRS, pp. 18–22, 2018.

V. V. Ryzhakov and E. V. Prohorova, “Electrocardiogram Signals Digital Processing in a Distributed Computing System,” Systems of Signal Synchronization, Generating and Processing in Telecommunications (SYNCHROINFO), 2020.

H. S. Anupama, N. K. Cauvery, and G. M. Lingaraju, “Real-time EEG based Object Recognition System Using Brain Computer Interface,” 2014 International Conference on Contemporary Computing and Informatics (IC3I), pp. 1046–1051, 2014.

J. Katona, T. Ujbanyi, G. Sziladi, and A. Kovari, “Speed Control of Festo Robotino Mobile Robot using NeuroSky MindWave EEG headset based Brain-Computer Interface,” 7th IEEE International Conference on Cognitive Infocommunications, pp. 251–256, Oct. 2016.

K. Tomonaga, S. Wakamizu, and J. Kobayashi, “Experiments on classification of electroencephalography (EEG) signals in imagination of direction using a wireless portable EEG headset,” 15th International Conference on Control, Automation and Systems : proceedings, pp. 1805–1810, 2015.

M. Alabboudi, M. Majed, F. Hassan, and A. B. Nassif, “EEG Wheelchair for People of Determination,” 2020.

P. Bala, R. Amob, M. Islam, S. Adib, F. Hasan, and M. N. Uddin, “EEG-Based Load Control System for Physically Challenged People,” in International Conference on Robotics, Electrical and Signal Processing Techniques, 2021, pp. 603–606. doi: 10.1109/ICREST51555.2021.9331050.

M. Alhammadi, B. Othman, S. R. B. Rasheed, T. Bonny, W. Al Nassan, and K. Obaideen, “Cursor Control Using electroencephalogram (EEG) Technology,” in 2022 International Conference on Electrical and Computing Technologies and Applications (ICECTA), 2022, pp. 415–419. doi: 10.1109/ICECTA57148.2022.9990531.

C. E. Yew and D. T. M. Yung, “Electroencephalography Robotic Arm Control,” in IEEE 4th International Symposium in Robotics and Manufacturing Automation (ROMA), 2018.

M. Staffa, M. Giordano, M. Berardinelli, M. De Gregorio, F. Ficuciello, and G. Acampora, “A Weightless Neural Network as a Classifier to translate EEG signals intoRobotic hand commands,” in 27th IEEE International Symposiumon Robot and Human Interactive Communication, 2018, pp. 487–490.

P. Sood and R. Dhiman, “Brain-Computer Interfacing: Design of Virtual Keyboard,” in 2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT), 2021, pp. 1–6. doi: 10.1109/ICCCNT51525.2021.9579519.

M. McMahon and M. Schukat, “A low-Cost, Open-Source, BCI-VR Game Control Development Environment Prototype for Game based Neurorehabilitation,” 29th Irish Signals and Systems Conference (ISSC), pp. 114–121, 2018.

D. Mattia, L. Astolfi, J. Toppi, M. Petti, F. Pichiorri, and F. Cincotti, “Interfacing Brain and Computer in Neurorehabilitation.” [Online]. Available:

P. G. Vinoj, S. Jacob, V. G. Menon, S. Rajesh, and M. R. Khosravi, “Brain-controlled adaptive lower limb exoskeleton for rehabilitation of post-stroke paralyzed,” IEEE Access, vol. 7, pp. 132628–132648, 2019, doi: 10.1109/ACCESS.2019.2921375.

M. Nafea, A. ’Aisha B. Hisham, N. A. Abdul-Kadir, and F. K. C. Harun, “Brainwave-Controlled System for Smart Home Applications,” ICBAPS : 2nd International Conference on BioSignal Analysis, Processing and Systems, pp. 75–80, 2018.

Chen Dongwei, Wu Fang, Wang Zhen, Li Haifang, and Chen Junjie, “Eeg-based emotion recognition with brain network using independent components analysis and granger causality,” in 2013 International Conference on Computer Medical Applications (ICCMA), IEEE, Jan. 2013, pp. 1–6. doi: 10.1109/ICCMA.2013.6506157.

D. Anwar, P. Garg, V. Naik, A. Gupta, and A. Kumar, “Use of Portable EEG Sensors to Detect Meditation.” [Online]. Available:

NeuroSky, “NeuroSky’s eSenseTM Meters and Detection of Mental State,” 2009.

A. Fauzi, T. Rijanto, and H. K. Wardana, “Pengendalian Peralatan Rumah Tangga Menggunakan Arduino Uno Berbasis Bluetooth,” Jurnal Reaktom, vol. 4, no. 1, pp. 39–44, 2019.

M. Rusdi and A. Yani, “Sistem Kendali Peralatan Elektronik Melalui Media Bluetooth Menggunakan Voice Recognition,” Journal of Electrical Technology, vol. 3, no. 1, pp. 27–33, 2018.

A. E. Hassanien and A. T. Azar, Brain-Computer Interfaces Current Trends and Applications, vol. 74. Springer, 2015. [Online]. Available: