Background: In various researches, ICA is used for detecting and removing eye artifacts but here, for innovation, ICA algorithm is used not only for detecting eye artifacts, but also for detecting brain signals of two conceptual categories of the words Danger and Information.

  Materials and Methods: In this descriptive- analytical study, recording is done by using a Micromed device and a 19-channel helmet in unipolar mode that the Cz electrode is selected as reference electrode. The statistical community included four men and four women in the age range of 25-30. In the designed task, three groups of traffic signs are considered in which two groups refered to the concept of danger and the other one refered to the concept of information.

  Results: For two of the eight volunteers, alpha waves were observed with a very high power from back of the head in the test time, but it was different in thinking time. According to this alpha waves, in changing the task from thinking to rest, it takes at least 3 and at most 5 seconds for two volunteers till they go to the absolute rest. For seven of the eight volunteers, danger and information signals well separated that these differences for five of the eight volunteers observed in the right hemisphere and for the other three volunteers in the left hemisphere.

  Conclusion: ICA algorithm as one of Blind Source Seperation (BSS) algorithms is suitable for recognizing the word’s concept and its place in the brain. Achieved results from this experiment are the same as the results from other methods like fMRI and methods based on electroencephalograph (EEG) in vowel imagination and covert speech.

Background: The purpose of this research is to design a Brain-Computer Interface to discriminate the brain signals while the brain images four main directions. To be innovative, the subjects have imaged the aimed directions by power of imagination, and for the first time, the ICA algorithm has been used to detect the aimed signal and to eliminate the artifacts.

Materials and Methods: In this descriptive-ana alytic study, signals are recorded by using a Micromed device and a 19-channel helmet in unipolar mode. The statistical population included three persons in the age range of 25 to 30 and the designed task consisted of 24 slides of four main directions.

Results: Simulations have shown that the best classification accuracy was the outcome of the 2.5-second time windowing and the best choice for extracting features was the AR coefficients of 15 order. There was no significant difference between the classification accuracy of different implementation of the Artificial Neural Network classifier with different number of layers and neurons and different classification functions. In comparison with the Neural Network, the Linear Discriminant Analysis (LDA) showed better classification accuracies.

Conclusion: The results of this research are in accordance with the results of the methods such as FMRI and methods based on the brain signals in vowel imagination. In this research, the best classification accuracy was obtained from the Linear Discriminant Analysis classifier by extracting the target signal from the output of the ICA algorithm and extracting the AR coefficients as feature and the 2.5-second time windowing. The Linear Discriminant Analysis classifier result the best classification accuracies.