A Single Electrode Blink for Text Interface (BCI)

Harshini, D; Ranjitha, M; Jadon, Rushali; NATARAJAN, .

doi:10.1109/inocon50539.2020.9298387

Cited by 3 publications

(3 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…2020. Natranjan, et al implemented a system that detected blink using single electrode electroencephalogram (EEG) headset and processed it to trigger keypress on default Windows on-screen keyboard [5]. This implementation has the drawback that latency is high, it takes too long to type sentences.…”

Section: B a Single Electrode Blink For Text Interface 2020 Ieee Inte...mentioning

confidence: 99%

“…Therefore, pre-processing of raw brain signals, extraction of significant features, and classification play a crucial role in the performance of the BMI system [3]. EEG headsets integrated with the ThinkGear chip facilitate signal processing and send the collected data to an open network socket due to the chip [5]. For convenience and ease of use, an EEG headset equipped with the same chip is chosen for this system.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Revolutionizing Human-Computer Interaction through Brain-Machine Interface: An EEG-Based Approach

Patole¹,

Sinha²,

Khairnar³

et al. 2023

IJRASET

View full text Add to dashboard Cite

Human-Machine Interface (HMI) devices, such as keyboards and mice, have been the primary means of input for computers since their inception. However, these devices have limitations that make them unsuitable for individuals with motor disabilities, motor impairment, or diseases such as paralysis, muscular dystrophy, polio, cerebral palsy, and others. This limits their ability to fully engage in computer-related activities, which can have a significant impact on their quality of life. This research paper proposes that new and emerging technologies, such as Brain-Machine Interface (BMI) and Machine Learning (ML), could be utilized to design a more convenient and accessible HMI solution that improves the quality of life for individuals with physical disabilities. BMI technology enables communication between the brain and external devices, while ML can analyse data and make predictions based on patterns and models. Combining these technologies can provide a more intuitive and adaptive interface that can detect and respond to the user's intentions and needs

show abstract

Section: B a Single Electrode Blink For Text Interface 2020 Ieee Inte...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Revolutionizing Human-Computer Interaction through Brain-Machine Interface: An EEG-Based Approach

Patole¹,

Sinha²,

Khairnar³

et al. 2023

IJRASET

View full text Add to dashboard Cite

show abstract

“…R. Jadon et al [9] in this paper, the methods used for preprocessing, feature extraction, classification, and conversion of signals into commands for typing done using the On-Screen Keyboard available on Windows Operating System by using single blinks is acquired from the NeuroSky MindWave headset. P. Bala et al [10], M. Rajmohan et al [11] represent a braincontrolled EEG system for home automation that is designed to help physically disable and paralyze people.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Thresholds of EEG Brain Signals for IoT Devices Authentication

Elshenaway¹,

Guirguis²

2021

IEEE Access

View full text Add to dashboard Cite

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.

show abstract