Using brain-computer-interface for robot arm control

Rosca, Sebastian; Leba, Monica

doi:10.1051/matecconf/201712108006

Cited by 14 publications

(2 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…BCI provides an opportunity for people with severe motor deficiency to play games in order to fulfill their psychological needs. BCI also provides opportunities for disabled patients to relax [51]. For example, a virtual reality game, MindBalance was developed at the University College, Dublin and Media Laboratory Europe for people with limited body movement.…”

Section: Entertainmentmentioning

confidence: 99%

Prospects and Problems of Brain Computer Interface in Healthcare

Olaronke¹,

Ikono²,

Gambo³

et al. 2018

CJAST

View full text Add to dashboard Cite

Brain-Computer Interface (BCI) otherwise known as a Brain-Machine Interface (BMI) is an emergent technology whose goal is to create a real-time and direct communication pathway between the brain and external devices such as computers, robots, artificial limbs and wheelchairs. In BCI, cerebral or brain activities control these devices by transmitting and receiving signals from the brain. BCI is applied in healthcare to improve the communication capabilities of people living with disabilities or locked in syndrome such as traumatic brain disorders, Amyotrophic Lateral Sclerosis (ALS), spinal cord injury, brain stem stroke and other severe motor disabilities. BCI also increases the independence of disabled individuals by improving their muscle control. Consequently, BCI improves the quality of life of disabled persons by allowing this group of people to live a normal and comfortable life. In spite of the benefits of BCI, the technology is not widely deployed in healthcare. This is because of the numerous challenges associated with it. One of the basic limitations of BCI is that the signals received from the brain are prone to interference. Furthermore, legal and ethical concerns such as the risk of infection or hemorrhage, psychological

show abstract

Section: Entertainmentmentioning

confidence: 99%

Prospects and Problems of Brain Computer Interface in Healthcare

Olaronke¹,

Ikono²,

Gambo³

et al. 2018

CJAST

View full text Add to dashboard Cite

show abstract

“…A BCI is defined as a device that measures the activity of the brain or central nervous system and converts these signals into artificial output [4]. A wide range of applications can apply knowledge of the EEG signal [5], but BCI is not an easy task. BCI research requires expertise and knowledge in many different fields such as signal processing, computer science, computational neuroscience, and embedded intelligent systems.…”

Section: Introductionmentioning

confidence: 99%

Frequency Band and PCA Feature Comparison for EEG Signal Classification

Pratama

Kesiman

Gunadi

2021

LKJITI

View full text Add to dashboard Cite

The frequency band method is popular in signal processing; this method separates EEG signals into five bands of frequency. Besides the frequency band, the recent research show PCA method gives a good result to classify digits number from EEG signal. Even PCA give a good accuracy to classify digit number from EEG signal, but there are no research shows which one yielded better accuracy between PCA and frequency band to classify digit number from EEG signals. This paper presents the comparison between those methods using secondary data from MindBigData (MDB). The result shows that the frequency band and PCA achieve 9% and 12,5% on average accuracy with the EPOC dataset. The paired Wilcoxon test produces a significant difference in accuracy between methods in the digit classification problem. Experiment with Muse dataset provides 31% accuracy with frequency band method and 24,8% with PCA method. The result is competitive compared to other experiments to classify digit numbers from EEG signals. In conclusion, there is no winner between the two methods since no method fits both datasets used in this research.

show abstract

Ankle Modeling and Simulation in the Context of Sport Activities

Leba

Marica

2020

Trends and Innovations in Information Systems and Technologies

View full text Add to dashboard Cite

Using brain-computer-interface for robot arm control

Cited by 14 publications

References 8 publications

Prospects and Problems of Brain Computer Interface in Healthcare

Prospects and Problems of Brain Computer Interface in Healthcare

Frequency Band and PCA Feature Comparison for EEG Signal Classification

Ankle Modeling and Simulation in the Context of Sport Activities

Contact Info

Product

Resources

About