A survey of signal processing algorithms in brain?computer interfaces based on electrical brain signals <s> Neural networks <s> Linear and nonlinear (TDNN) models have been shown to estimate hand position using populations of action potentials collected in the pre-motor and motor cortical areas of a primate's brain. One of the applications of this discovery is to restore movement in patients suffering from paralysis. For real-time implementation of this technology, reliable and accurate signal processing models that produce small error variance in the estimated positions are required. In this paper, we compare the mapping performance of the FIR filter, gamma filter and recurrent neural network (RNN) in the peaks of reaching movements. Each approach has strengths and weaknesses that are compared experimentally. The RNN approach shows very accurate peak position estimations with small error variance. <s> BIB001 </s> A survey of signal processing algorithms in brain?computer interfaces based on electrical brain signals <s> Neural networks <s> The ability to record, in real-time, the activity of hundreds of cortical neurons gives the ability to selectively study the function of clusters of cortical neurons in Brain Machine Interface (BMI) experiments. We have demonstrated using a recursive multilayer perceptron (RMLP) that using the appropriate signal processing theory in a well-chosen parsimonious model, we can develop constructs that agree with basic physiological modeling of neural control. By looking through the trained model, we have found interesting relationships between the neuronal firing and the movement. The RMLP allows us to continuously study the relationship between neural activity and behavior without the active interference of the experimenter. The findings presented in this study offer an opportunity for the neuroscience community to compare the cortical interactions as constructed by the RMLP to what is known about motor neurophysiology. <s> BIB002
Recurrent MLP neural  BIB001 BIB002 use threshold detectors as the feature classifier or as part of the feature classification scheme (27%), linear discriminant (either LDA or FLD) classifiers (26%), those that show continuous feedback of the extracted features (16%), and those that use support vector machines (SVM) (11%). 27% of the neural-network-based classifiers are based on the multi-layer perceptrons (MLP) neural network and 39% are based on learning-vector-quantization (LVQ) classification scheme. During our analysis of the literature, a number of salient points about the signal processing methods emerged. We think that some of these points are worth sharing with the BCI research community. In the following sections we summarize some of them. Note that these observations are based on comments made by the researchers in their published papers and are also based on the trends in the literature; they do not cover all the methods reported in the literature. 