Normalization of feature distribution in motor imagery based brain-computer interfaces

Brain-Computer Interfaces (BCIs) are systems capable of capturing and interpreting the consent changes in the activity of brain (e.g. intention of limb movement, attention focus on specific frequency or symbol) and translating them into sets of instructions, which can be used for the control of a computer. The most popular hardware solutions in BCI are based on the signals recorded by the electroencephalograph (EEG). Such signals can be used to record and monitor the bioelectrical activity of the brain. However, raw EEG scalp potentials are characterized by a weak spatial resolution. Due to that reason, multichannel EEG recordings tend to provide an unclear image of the activity of brain and the use of special signal processing and analysis methods is needed. A typical approach towards modern BCIs requires an extensive use of Machine Learning methods. It is generally accepted that the performance of such systems is highly sensitive to the feature extraction step. One of the most effective and widely used descriptors of EEG data is the power of the signal calculated in a specific frequency range. In order to improve the performance of chosen classification algorithm, the distribution of the extracted bandpower features is often normalized with the use of natural logarithm function. In this study the step of normalization of feature distribution was taken into careful consideration. Commonly used logarithm function is not always the best choice for this process. Therefore, the influence on the skewness of features, as well as, on the general classification accuracy of different settings of Box-Cox transformation will be tested in this article and compared to classical approach that employs natural logarithm function. For the better evaluation of the performance of the proposed approach, its effectiveness is tested in the task of classification of the benchmark data provided for the 'BCI Competition III' (dataset 'IVa') organized by the Berlin Brain-Computer Interface group.