LabVIEW and Android BCI Chat App Controlled By Voluntary Eye-Blinks Using NeuroSky Mindwave Mobile EEG Headset

Rușanu, Oana Andreea; Cristea, Luciana; Luculescu, Marius Cristian

doi:10.1109/ehb50910.2020.9280193

Cited by 5 publications

(3 citation statements)

References 6 publications

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“…The only EEG recording electrode of the MindWave Mobile is located on the forehead above the eye (FP1 position according to the 10-20 International System) with a reference electrode and grounding electrode inside the ear clip. Therefore, the MindWave Mobile EEG headset is commonly used for eye blink detection [ 72 – 75 ]. The device outputs 12-bit 3- to 100-Hz original brainwaves with a sampling rate of 512 Hz and can achieve an 8-h battery run time.…”

Section: Forehead-eeg-based Bcimentioning

confidence: 99%

Recent Progress in Wearable Brain–Computer Interface (BCI) Devices Based on Electroencephalogram (EEG) for Medical Applications: A Review

Zhang,

Li,

Huang

et al. 2023

Health Data Sci

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Importance: Brain–computer interface (BCI) decodes and converts brain signals into machine instructions to interoperate with the external world. However, limited by the implantation risks of invasive BCIs and the operational complexity of conventional noninvasive BCIs, applications of BCIs are mainly used in laboratory or clinical environments, which are not conducive to the daily use of BCI devices. With the increasing demand for intelligent medical care, the development of wearable BCI systems is necessary. Highlights: Based on the scalp-electroencephalogram (EEG), forehead-EEG, and ear-EEG, the state-of-the-art wearable BCI devices for disease management and patient assistance are reviewed. This paper focuses on the EEG acquisition equipment of the novel wearable BCI devices and summarizes the development direction of wearable EEG-based BCI devices. Conclusions: BCI devices play an essential role in the medical field. This review briefly summarizes novel wearable EEG-based BCIs applied in the medical field and the latest progress in related technologies, emphasizing its potential to help doctors, patients, and caregivers better understand and utilize BCI devices.

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Section: Forehead-eeg-based Bcimentioning

confidence: 99%

Recent Progress in Wearable Brain–Computer Interface (BCI) Devices Based on Electroencephalogram (EEG) for Medical Applications: A Review

Zhang,

Li,

Huang

et al. 2023

Health Data Sci

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“…O. A. Rușanu et al [6] proposed a simple chat application based on the counting of the voluntary eye-blinks acquired by a NeuroSky headset. One eye-blink determines the transition across emoticons/buttons, while two eye-blinks enable the selection of the emoticon corresponding to the desired information.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Thresholds of EEG Brain Signals for IoT Devices Authentication

Elshenaway¹,

Guirguis²

2021

IEEE Access

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In this paper, we proposed a new authentication method for the Internet of Things IoT devices based on electroencephalography EEG signals, and hand gestures. The proposed EEG signals authentication method used low price NeuroSky MindWave headset and was based on choosing the adaptive thresholds of attention and meditation mode for the authentication key. Hand gestures to control authentication processes by using a general camera. To verify that a new authentication method is widely accepted, it must meet two main conditions security and usability. The evaluation of the prototype usability was based on ISO 9241-11:2018 standards usability model. Results revealed that our proposed demonstrated the ability of authentication by using EEG signals with the effectiveness of 92%, the efficiency of 93%, and User-Satisfaction is acceptable and satisfying. To assess evaluate the security of the prototype, we consider the most important three threats related to IoT devices are guessing, physical observation, and targeted impersonation. The results showed that the password strength, using the proposed system is stronger than that with a traditional keyboard, the proposed authentication method resistant to target impersonation and physical observation.

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“…The user experience was enhanced by dividing the selected block into sub-blocks for further selection; however, the average processing time of more than one minute to print a single alphabet was time-consuming for communicating with a user. Similarly, Rusanu et al proposed a communication scheme using EEG [8]. It was designed to speed up communication with users by having them blink to select one of 32 predetermined emoticons rather than alphabet letters, and it also showed limitations in expressing something other than the predetermined emoticons.…”

Section: Introductionmentioning

confidence: 99%

iMouse: Augmentative Communication with Patients Having Neuro-Locomotor Disabilities Using Simplified Morse Code

2023

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Patients with amyotrophic lateral sclerosis (ALS), also known as Lou Gehrig’s disease, an incurable disease in which motor neurons are selectively destroyed, gradually lose their mobility as organ dysfunction begins, and eventually, patients find it challenging to make even minor movements and simple communications. To communicate with patients with quadriplegia, researchers have focused on movements of the eye, the only moving organ for patients with ALS, and they have investigated the detection of eyeblinks using brainwaves or cameras or other ways to select letters on a screen via eyeball movements based on eye-tracking cameras. However, brainwave-based techniques, which use the electrical signals of eye movements to determine patient’s intentions, are sensitive to noise, often resulting in the inaccurate identification of intent. Alternatively, a camera-based method that uses letter selection detects the movement of eye feature-points, and this method makes it easy to identify a patient’s intentions using a predefined decision-making process. However, it has long processing time and is prone to inaccuracy due to errors in either the Morse code implementation assigned to all alphabets or the sequential selection methods. Therefore, we have proposed iMouse-sMc, a simplified Morse code-based user interface model using an eye mouse for faster and easier communication with such patients. Furthermore, we improved the detection performance of the eye mouse by applying image contrast techniques to enable communication with patients even at night. To verify the excellent performance of the proposed eye mouse for a user interface, we conducted comparative experiments with existing camera-based communication models based on various words. The results revealed that the time of communication was reduced to 83 s and the intention recognition accuracy was improved by ~28.16%. Additionally, even in low-light environments, where existing models are unable to communicate with the patients due to difficulties with eye detection, the proposed model demonstrated its eye detection capability and proved that it can be used universally for communication with patients during the day and at night.

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LabVIEW and Android BCI Chat App Controlled By Voluntary Eye-Blinks Using NeuroSky Mindwave Mobile EEG Headset

Cited by 5 publications

References 6 publications

Recent Progress in Wearable Brain–Computer Interface (BCI) Devices Based on Electroencephalogram (EEG) for Medical Applications: A Review

Recent Progress in Wearable Brain–Computer Interface (BCI) Devices Based on Electroencephalogram (EEG) for Medical Applications: A Review

Adaptive Thresholds of EEG Brain Signals for IoT Devices Authentication

iMouse: Augmentative Communication with Patients Having Neuro-Locomotor Disabilities Using Simplified Morse Code

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