TOPICAL REVIEW A Review of Classification Algorithms for EEG-based Brain-Computer Interfaces <s> k Nearest Neighbors <s> Driven by the progress in the field of single-trial analysis of EEG, there is a growing interest in brain computer interfaces (BCIs), i.e., systems that enable human subjects to control a computer only by means of their brain signals. In a pseudo-online simulation our BCI detects upcoming finger movements in a natural keyboard typing condition and predicts their laterality. This can be done on average 100-230ms before the respective key is actually pressed, i.e., long before the onset of EMG. Our approach is appealing for its short response time and high classification accuracy (>96%) in a binary decision where no human training is involved. We compare discriminative classifiers like Support Vector Machines (SVMs) and different variants of Fisher Discriminant that possess favorable regularization properties for dealing with high noise cases (inter-trial variablity). <s> BIB001 </s> TOPICAL REVIEW A Review of Classification Algorithms for EEG-based Brain-Computer Interfaces <s> k Nearest Neighbors <s> The low-frequency asynchronous switch design (LF-ASD) was introduced as a direct brain-computer interface (BCI) technology for asynchronous control applications. The LF-ASD operates as an asynchronous brain switch (ABS) which is activated only when a user intends control and maintains an inactive state output when the user is not meaning to control the device (i.e., they may be idle, thinking about a problem, or performing some other action). Results from LF-ASD evaluations have shown promise, although the reported error rates are too high for most practical applications. This paper presents the evaluation of four new LF-ASD designs with data collected from individuals with high-level spinal cord injuries and able-bodied subjects. These new designs incorporated electroencephalographic energy normalization and feature space dimensionality reduction. The error characteristics of the new ABS designs were significantly better than the LF-ASD design with true positive rate increases of approximately 33% for false positive rates in the range of 1%-2%. The results demonstrate that the dimensionality of the LF-ASD feature space can be reduced without performance degradation. The results also confirm previous findings that spinal cord-injured subjects can operate ABS designs to the same ability as able-bodied subjects. <s> BIB002 </s> TOPICAL REVIEW A Review of Classification Algorithms for EEG-based Brain-Computer Interfaces <s> k Nearest Neighbors <s> To determine and compare the performance of different classifiers applied to four-class EEG data is the goal of this communication. The EEG data were recorded with 60 electrodes from five subjects performing four different motor-imagery tasks. The EEG signal was modeled by an adaptive autoregressive (AAR) process whose parameters were extracted by Kalman filtering. By these AAR parameters four classifiers were obtained, namely minimum distance analysis (MDA)--for single-channel analysis, and linear discriminant analysis (LDA), k-nearest-neighbor (kNN) classifiers as well as support vector machine (SVM) classifiers for multi-channel analysis. The performance of all four classifiers was quantified and evaluated by Cohen's kappa coefficient, an advantageous measure we introduced here to BCI research for the first time. The single-channel results gave rise to topographic maps that revealed the channels with the highest level of separability between classes for each subject. Our results of the multi-channel analysis indicate SVM as the most successful classifier, whereas kNN performed worst. <s> BIB003
The aim of this technique is to assign to an unseen point the dominant class among its k nearest neighbors within the training set  . For BCI, these nearest neighbors are usually obtained using a metric distance, e.g., BIB001 . With a sufficiently high value of k and enough training samples, kNN can approximate any function which enables it to produce nonlinear decision boundaries. KNN algorithms are not very popular in the BCI compmunity, probably because they are known to be very sensitive to the curse-of-dimensionality  , which made them fail in several BCI experiments BIB003 [38]  . However, when used in BCI systems with low-dimensional feature vectors, kNN may prove to be efficient BIB002 .