In this study, we have carried out a brain-computer interface study that uses time delays between electrodes. As features, we have calculated the time delays that maximizes the absolute value of the cross-covariance between the chosen reference channel and the remaining channels. Performance results of 3 out of 5 participants that were nearly at a %100 accuracy level along with a relatively smaller number of training data, and a lack of similar studies indicate that the proposed approach is open to the further improvements.

In this paper, handwritten character recognition by using characters' writing movements is investigated. To obtain the information about writing movements a 3-axis accelerometer is used. Just like most of other sensors, 3-axis accelerometers give the actual movement signal as well as noise. Before the recognition step, all of the signals need to be preprocessed and the noisy parts need to be removed. So, Empirical Mode Decomposition (EMD) and normalization preprocessing steps are applied to the signals. Finally, the signals in the dataset are compared with Dynamic Time Warping for classification and accurate classification rate of 91.92% is obtained.