A brain-computer interface (BCI) is a device that enables direct communication between humans and computers by analyzing neural signals and transforming them into digital signals. A new brain-computer interface system based on the gaze on rotating vane-dependent EEG signal is presented. Classification of EEG signals is done in three sessions: 1-when vane rotates fast and slow in an anticlockwise manner, 2-when vane rotates slow in a clockwise and rotates fast in an anti-clockwise manner, 3-when vane rotates slow in a clockwise and rotates slow in an anti-clockwise manner. The features are extracted from the 1-sec epoch of the EEG using Fast Fourier Transform (FFT). We use k-nearest neighbor (k_NN) algorithm to classify these features. The proposed method is also applied to 2-sec, 3-sec, and 4-sec epochs. All the signals are obtained at department of electrical and electronics engineering, Karadeniz Technical University, from 8 healthy human subjects in age groups between 20 and 32 years old. The proposed algorithm is efficient in the classification phase, with the obtained accuracy of 56-94% for eight subjects in 1-sec epochs. The results show that the proposed BCI system is very fast and accurate.