Adaptive Symmetrical Virtual Keyboard Based on EOG Signal

Rusydi, Muhammad Ilhamdi; Anandika, Arrya; Adnan, Ramli; Matsuhita, Kojiro; Sasaki, Minoru

doi:10.1109/acirs.2019.8935956

Cited by 3 publications

(3 citation statements)

References 6 publications

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“…The virtual keyboard is designed to have 7×7 dimensions of buttons, so this keyboard has 49 buttons. This design is based on the virtual keyboard's character requirements and considers its symmetrical size [14]. There is a button in the keyboard's center, without special characters or commands to the system.…”

Section: Methodsmentioning

confidence: 99%

“…Meanwhile, vertical movements include eye movements up and down [12], [13]. Utilization of EOG signals is one method that has been widely developed in establishing communication between humans and machines [12], [14]. The development of EOG research has been carried out with various implementations, such as virtual keyboards as communication aids [8], [14]- [17], robot manipulator control [4], wheelchair control [18], [19], robot control [1], [20], mouse control [5], game development [2], IoT Application development [21], internet browsing application development [22], alarm system [23] and other developments.…”

Section: Introductionmentioning

confidence: 99%

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Enhancing Interface Efficiency: Adaptive Virtual Keyboard Minimizing Keystrokes in Electrooculography-Based Control

Anandika,

Laksono,

Suhaimi

et al. 2023

JNTE

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Rapid technological developments, one of which is technology to build communication relationships between humans and machines using Biosignals. One of them is Electrooculography (EOG). EOG is a type of biosignals obtained from eye movement. Research related to EOG has also developed a lot, especially for virtual keyboard control. Research on virtual keyboard control based on eye gaze motion using electrooculography technology has been widely developed. Previous research mostly drew conclusions based on time consumption in typing paragraphs. However, it has not been seen based on the number of eye gaze motions made by the user. In this research, an adaptive virtual keyboard system is built, controlled using EOG signals. The adaptive virtual keyboard is designed with 7x7 dimensions and has 49 buttons, including main buttons, letters, numbers, symbols, and unused buttons. The layout of the adaptive virtual keyboard has six zones. Each zone has a different number of steps. Characters located in the same zone have the same number of steps. The adaptive feature is to rearrange the position of the character's button based on the previously used characters. In the experiments, 30 respondents controlled static and adaptive virtual keyboards with 7 paragraphs typed. Adaptive mode rearranges the position of buttons based on k-selection activities from respondents. the k numbers are 10, 30, 50, 70 and 100. Two virtual keyboard modes are evaluated based on the number of steps required to type the paragraphs. Test results show that the performance of the adaptive virtual keyboard can shorten the number of user steps compared to static mode. There are tests of the optimal system that can be reduced up to 283 number of steps and from respondents, that can reduced up to 258 number of steps or about 40% of steps. This research underscores the promise of EOG-driven adaptive virtual keyboards, signaling a notable stride in augmenting user interaction efficiency in typing experiences, heralding a promising direction for future human-machine interface advancements.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Enhancing Interface Efficiency: Adaptive Virtual Keyboard Minimizing Keystrokes in Electrooculography-Based Control

Anandika,

Laksono,

Suhaimi

et al. 2023

JNTE

Self Cite

View full text Add to dashboard Cite

show abstract

“…Another example is the application of biosignal technology (biosignal) to build communication between humans and computers or machines. There are several types of biosignals, including Electrooculograph (EOG) to detect eye movements [11], Electromyograph (EMG) to detect muscle movements [3], [12], Electrocardiograph (ECG) to detect heartbeats [13] and Electroencephalograph (EEG) by detecting brain signals [14]- [20].…”

Section: Introductionmentioning

confidence: 99%

Smart Room System for Paralysis Patients with Mindwave EEG Sensor Control

Anandika,

Ferdian,

Eriyandha

et al. 2023

JOIV : Int. J. Inform. Visualization

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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

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