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.

Background: ALS is a progressive neuro-muscular disease, which is characterized by motor neuron loss in the Central Nervous System (CNS) and Peripheral Nervous System (PNS). Up to now, no accurate clinical method for diagnosis of the disease have been provided. In most cases, ALS patients are unable to walk normally due to abnormalities in the nervous system. For this reason, one of the most appropriate methods in the diagnosis of ALS from other neurological diseases or from healthy volunteers is the gait motor signal analysis.

Materials and Methods: In this study, gait signals available in Physionet database have been used. The database consists of 13 patients with ALS (ALS1, ALS2, …, ALS13) and 16 normal subjects (CO1, CO2, …, CO16). The patients participating in this study had no history of any psychiatric disorders and did not use any assistive device for walking, like wheelchair. The power spectrum of stride, swing, and stance of normal subjects and patients was computed for both left and right legs. To provide appropriate inputs for the classifier, the frequency band of the power spectrum of all signals was divided into eight equal parts. The area of all regions was computed. Three frequency band of the lower range of power spectra selected as inputs of the classifier.

Results: In this study, power spectra, as frequency attributes, were used to explore probable differences of time series in both patients and healthy subjects.

Conclusion: Artificial Neural Network was used to classify normal and ALS groups with the accuracy of 83% for the test data set. It seems that the present algorithm can be used in discriminating patients from normal subjects in the early stages of the disease.