An application of online ANFIS classifier for wheelchair based brain computer interface

Turnip, Mardi; Dharma, Abdi; Pasaribu, Hendra; Harahap, Mawaddah; Amri, M. Faizal; Suhendra, M. Agung; Turnip, Arjon

doi:10.1109/icacomit.2015.7440192

Cited by 8 publications

(5 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Spelling applications are our key focus of this review. Nevertheless, it is worth mentioning that BCIs are applied for a lot of other purposes, e.g., robot control [ 115 , 116 , 117 ], wheelchair control [ 118 , 119 ], web browsing [ 120 ], general control of an operating system with a virtual keyboard, as presented in [ 121 ], gaming [ 122 ], every-day electronic device control [ 123 , 124 , 125 ], and ADHD attention training [ 126 ], just to mention a few. A lot of these applications are also useful for people with neuromuscular malfunctions, and a number of them can be suitable for healthy users.…”

Section: Discussionmentioning

confidence: 99%

Brain–Computer Interface Spellers: A Review

et al. 2018

View full text Add to dashboard Cite

A Brain–Computer Interface (BCI) provides a novel non-muscular communication method via brain signals. A BCI-speller can be considered as one of the first published BCI applications and has opened the gate for many advances in the field. Although many BCI-spellers have been developed during the last few decades, to our knowledge, no reviews have described the different spellers proposed and studied in this vital field. The presented speller systems are categorized according to major BCI paradigms: P300, steady-state visual evoked potential (SSVEP), and motor imagery (MI). Different BCI paradigms require specific electroencephalogram (EEG) signal features and lead to the development of appropriate Graphical User Interfaces (GUIs). The purpose of this review is to consolidate the most successful BCI-spellers published since 2010, while mentioning some other older systems which were built explicitly for spelling purposes. We aim to assist researchers and concerned individuals in the field by illustrating the highlights of different spellers and presenting them in one review. It is almost impossible to carry out an objective comparison between different spellers, as each has its variables, parameters, and conditions. However, the gathered information and the provided taxonomy about different BCI-spellers can be helpful, as it could identify suitable systems for first-hand users, as well as opportunities of development and learning from previous studies for BCI researchers.

show abstract

Section: Discussionmentioning

confidence: 99%

Brain–Computer Interface Spellers: A Review

et al. 2018

View full text Add to dashboard Cite

show abstract

“…The measurement of the test is seen from the comparison of the average error of the different membership types. Where it is produced that ANFIS can be used to process academic performance assessments [19], [20].…”

Section: Anfis Methodsmentioning

confidence: 99%

Academic Performance Assessment Prediction Model Using the Adaptive Neuro-Fuzzy Inference System Method

Turnip,

Almahdi,

Tampubolon

et al. 2023

j. of artif. intell. and eng. appl.

View full text Add to dashboard Cite

Performance evaluation of Human Resources is an important part of an organization or company. One of the HR in tertiary institutions that must be evaluated is academic performance. The author conducted research on the factors of academic performance assessment. Therefore, we need a system that can classify academic performance optimally in order to improve the quality of academic performance. In this research, we construct a predictive model for academic ability evaluation using Adaptive Neuro-Fuzzy Inference System method. The ANFIS method shows a very good data accuracy of 92.20%. From each variable motivation to work appraisal show is a Good, competence to work appraisal = Good, compensation to work appraisal show is a Good, responsibility to work appraisal show is a Good, and job satisfaction to work assessment show is Good.

show abstract

“…The third and fourth methods [147,148], utilizing ANFIS (Adaptive Neuro-Fuzzy Inference System) for EEG signal classification in detecting epileptic seizures and in brain-computer interfacing, respectively, demonstrate the applicability of fuzzy techniques in realtime EEG signal processing. These approaches emphasize the ability of fuzzy techniques to provide accurate analysis and fine modulation of the artifact removal process, maintaining the integrity of the original signal for more precise diagnoses and advanced applications such as device control via BCI.…”

Section: Fuzzy Approaches For the Removal Of Artifacts: Case Studiesmentioning

confidence: 99%

EEG Data Analysis Techniques for Precision Removal and Enhanced Alzheimer’s Diagnosis: Focusing on Fuzzy and Intuitionistic Fuzzy Logic Techniques

Versaci,

La Foresta

2024

Signals

View full text Add to dashboard Cite

Effective management of EEG artifacts is pivotal for accurate neurological diagnostics, particularly in detecting early stages of Alzheimer’s disease. This review delves into the cutting-edge domain of fuzzy logic techniques, emphasizing intuitionistic fuzzy systems, which offer refined handling of uncertainties inherent in EEG data. These methods not only enhance artifact identification and removal but also integrate seamlessly with other AI technologies to push the boundaries of EEG analysis. By exploring a range of approaches from standard protocols to advanced machine learning models, this paper provides a comprehensive overview of current strategies and emerging technologies in EEG artifact management. Notably, the fusion of fuzzy logic with neural network models illustrates significant advancements in distinguishing between genuine neurological activity and noise. This synthesis of technologies not only improves diagnostic accuracy but also enriches the toolset available to researchers and clinicians alike, facilitating earlier and more precise identification of neurodegenerative diseases. The review ultimately underscores the transformative potential of integrating diverse computational techniques, setting a new standard in EEG analysis and paving the way for future innovations in medical diagnostics.

show abstract

An application of online ANFIS classifier for wheelchair based brain computer interface

Cited by 8 publications

References 12 publications

Brain–Computer Interface Spellers: A Review

Brain–Computer Interface Spellers: A Review

Academic Performance Assessment Prediction Model Using the Adaptive Neuro-Fuzzy Inference System Method

EEG Data Analysis Techniques for Precision Removal and Enhanced Alzheimer’s Diagnosis: Focusing on Fuzzy and Intuitionistic Fuzzy Logic Techniques

Contact Info

Product

Resources

About