Usage of Fuzzy Classification Algorithms in Brain-Computer Interfaces
نویسنده
چکیده
In this thesis, the usage of fuzzy classification algorithms in brain-computer interfaces (BCI) based on electroencephalography (EEG) is researched. We review the existing literature on BCI, the traditional crisp algorithms often used in BCI for classification, fuzzy classification algorithms and their application in BCI. A simple BCI system is implemented that allows the user to move a cursor on the computer screen. Tests conducted with this application show that fuzzy classification algorithms do not have advantage over crisp classification algorithms in this kind of BCI systems.
منابع مشابه
Proposing a Novel Cost Sensitive Imbalanced Classification Method based on Hybrid of New Fuzzy Cost Assigning Approaches, Fuzzy Clustering and Evolutionary Algorithms
In this paper, a new hybrid methodology is introduced to design a cost-sensitive fuzzy rule-based classification system. A novel cost metric is proposed based on the combination of three different concepts: Entropy, Gini index and DKM criterion. In order to calculate the effective cost of patterns, a hybrid of fuzzy c-means clustering and particle swarm optimization algorithm is utilized. This ...
متن کاملINTERVAL ANALYSIS-BASED HYPERBOX GRANULAR COMPUTING CLASSIFICATION ALGORITHMS
Representation of a granule, relation and operation between two granules are mainly researched in granular computing. Hyperbox granular computing classification algorithms (HBGrC) are proposed based on interval analysis. Firstly, a granule is represented as the hyperbox which is the Cartesian product of $N$ intervals for classification in the $N$-dimensional space. Secondly, the relation betwee...
متن کاملEEG Based Brain Computer Interface Hand Grasp Control: Feature Extraction Method MTCSP
Brain-Computer Interfaces (BCIs) are communication systems, which enable users to send commands to computers by using brain activity only; this activity being generally measured by Electroencephalography (EEG). BCIs are generally designed according to a pattern recognition approach, i.e., by extracting features from EEG signals, and by using a classifier to identify the user’s mental state from...
متن کاملEEG Based Brain Computer Interface Hand Grasp Control: Feature Extraction Method MTCSP
Brain-Computer Interfaces (BCIs) are communication systems, which enable users to send commands to computers by using brain activity only; this activity being generally measured by Electroencephalography (EEG). BCIs are generally designed according to a pattern recognition approach, i.e., by extracting features from EEG signals, and by using a classifier to identify the user’s mental state from...
متن کاملEEG Based Patient Monitoring System for Mental Alertness Using Adaptive Neuro-Fuzzy Approach
Recent electrophysiological studies support command-specific changes in the electroencephalography (EEG) that have promoted their intensive application in the noninvasive brain computer interfaces (BCI). However, EEG is plagued by a variety of interferences and noises, thereby demanding better accuracy and stability for its application in the neuroprosthetic devices. Here we investigate wavelet...
متن کامل