Brain-Computer Interface (BCI) is a technology that utilizes brainwaves to link the brain with external machines for either medical analysis, or to improve quality of life such as control and communication for people affected with paralysis. The performance of BCI systems depends on classification accuracy, which influences the Information Transfer Rate. This motivates researchers to improve their classification accuracy as best possible. A bias problem in reporting accuracies by using non-nested cross-validation methods was thought to increase accuracy. The aim of this paper was to validate and quantify such a concept by using a low-cost commercial EEG recorder to classify visually evoking face vs scrambled pictures, and report high accuracy using non-nested cross validation. The algorithm employed Independent Component Analysis followed by feature extraction with sample covariance matrices. The data were then
classified using Support Vector Machines. The accuracy was tested with nested and non-nested cross-validation methods; accuracies obtained were 63% and 76%, respectively.