6 results for “Brain-computer interface”

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Research ArticleOpen accessPhrase in the titleEnhancing P300 Component by Spectral Power Ratio Principal Components for a Single Trial Brain-Computer InterfaceS. Andrews, Ramaswamy Palaniappan, Andrew Teoh, Loo chu KiongHere we present a novel approach to detect P300 wave in single trial Visual Event Related Potential (VERP) signals using improved principal component analysis to enable a faster brain-computer interface (BCI) design. In the process, the principal components (PCs) are selected using novel methods, namely spectral power ratio (SPR) and sandwich spectral power ratio (SSPR). We set out to assess … …American Journal of Applied Sciences, Vol. 5, Issue 6 · 30 Jun 2008 · 7,881 accessesView PDF Cite

Research ArticleOpen accessEvery word in the titleClassifying Single Trail Electroencephalogram Using Gaussian Smoothened Fast Hartley Transform for Brain Computer Interface during Motor ImageryV. B. Deepa, P. Thangaraj, S. ChitraProblem statement: Brain-Computer Interface (BCI) is a emerging research area which translates the brain signals for any motor related actions into computer understandable signals by capturing the signal, processing the signal and classifying the motor imagery. This area of work finds various applications in neuroprosthetics. Mental activity leads to changes of electrophysiological signals like the Electroencephalogram (EEG) or Electrocorticogram (ECoG). … …Journal of Computer Science, Vol. 7, Issue 5 · 7 May 2011 · 6,658 accessesView PDF Cite

Research ArticleOpen accessEvery word in the titleComparison of Pre-Processing and Classification Techniques for Single-Trial and Multi-Trial P300-Based Brain Computer InterfacesChanan S. Syan, Randy E.S. HarnarinesinghThe P300 component of Event Related Brain Potentials (ERP) is commonly used in Brain Computer Interfaces (BCI) to translate the intentions of an individual into commands for external devices. The P300 response, however, resides in a signal environment of high background noise. Consequently, the main problem in developing a P300-based BCI lies in identifying the P300 response in the presence … …American Journal of Applied Sciences, Vol. 7, Issue 9 · 30 Sep 2010 · 7,570 accessesView PDF Cite

Review ArticleOpen accessIn the keywordsProgress and Challenges of Neuroscience and Brain-inspired Artificial IntelligenceLidong Wang, Cheryl Ann AlexanderNeuroscience and brain-inspired artificial intelligence are significant research areas. Many countries have launched brain-related projects in which neuroscience and brain-inspired artificial intelligence are major targeted areas to increase national interests and enhance their strength in key areas such as military and homeland security in the competitive global world. Methods, emerging technologies, and progress in neuroscience and brain-inspired artificial intelligence are … …Neuroscience International, Vol. 11, Issue 1 · 4 Feb 2020 · 7,835 accessesView PDF Cite

Research ArticleOpen accessIn the abstractEnhanced Support Vector Machine based on Metaheuristic Search for Multiclass ProblemsAbdullah Saeed Ghareb, Mohammed Hamid Afif, Abdel-Rahman Hedar et al.Machine learning is an important field of artificial intelligent researches and it highly growing for real intelligent applications systems that relate brain computer interface to human brain activities. Support Vector Machine (SVM) is a popular machine learning approach, which can be used for pattern recognition, prediction and classification with many diverse applications. However, the SVM has many parameters, which have … …Journal of Computer Science, Vol. 16, Issue 7 · 9 Jul 2020 · 6,906 accessesView PDF Cite

Research ArticleOpen accessIn the abstractComparative Evaluation of Adaptive Filter and Neuro-Fuzzy Filter in Artifacts Removal From Electroencephalogram SignalPaulchamy Balaiah, IlavennilaProblem statement: This study presents an effective method for removing mixed artifacts (EOG-Electro-ocular gram, ECG-Electrocardiogram, EMG-Electromyogram) from the EEG-Electroencephalogram records. The noise sources increases the difficulty in analyzing the EEG and obtaining clinical information. EEG signals are multidimensional, non-stationary (i.e., statistical properties are not invariant in time), time domain biological signals, which are not reproducible. It is supposed to contain … …American Journal of Applied Sciences, Vol. 9, Issue 10 · 15 Aug 2012 · 7,087 accessesView PDF Cite