Human visual system recognizes objects in a fast manner and the neural activity of the human brain generates signals which provide information about objects categories seen by the subjects. The brain signals can be recorded using different systems like the electroencephalogram (EEG). The EEG signals carry significant information about the stimuli that stimulate the brain. In order to translate information derived from the EEG for the object recognition mechanism, in this study, twelve various categories were selected as visual stimuli and were presented to the subjects in a controlled task and the signals were recorded through 19-channel EEG recording system. Analysis of signals was performed using two different event-related potential (ERP) computations namely the “target/rest” and “target/non-target” tasks. Comparing ERP of target with rest time indicated that the most involved electrodes in our task were F3, F4, C3, C4, Fz, Cz, among others. ERP of “target/non-target” resulted that in target stimuli two positive peaks occurred about 400 ms and 520 ms after stimulus onset; however, in non-target stimuli only one positive peak appeared about 400 ms after stimulus onset. Moreover, reaction times of subjects were computed and the results showed that the category of flower had the lowest reaction time; however, the stationery category had the maximum reaction time among others. The results provide useful information about the channels and the part of the signals that are affected by different object categories in terms of ERP brain signals. This study can be considered as the first step in the context of human-computer interface applications.

Since EEG signals encode an individual’s intent of executing an action, scientists have extensively focused on this topic. Motor Imagery (MI) signals have been used by researchers to assistance disabled persons, for autonomous driving and even control devices such as wheelchairs. Therefore, accurate decoding of these signals is essential to develop a Brain–Computer interface (BCI) systems. Due to dynamic nature, low signal-to-noise ratio and complexity of EEG signals, EEG decoding is not simple task. Extracting temporal and spatial features from EEG is accessible via Convolution neural network (CNN). However, enhanced CNN models are required to learn the dynamic correlations existing in MI signals. It is found that good features are extracted via CNN in both deep and shallow models, which indicate that various levels related features can be mined. In this case, spatial patterns from multi-scaled data in different frequency bands are learnt at first and then the temporal and frequency band information from projected signals is extracted. Here, to make use of neural activity phenomena, the feature extraction process employed is based on Multi-scale FBCSP (MSFBCSP). In CNN, the envelope of each spatially filtered signal is extracted in time dimension by performing Hilbert transform. However, to access common morphologies, the convolutional operation across time is performed first and then another convolution layer across channels in the frequency band is used to represent the carried information in a more compact form. Moreover, Bayesian approach is used for mapping hyperparameters to a probability of score on the objective function. The prominent feature of the proposed network is the high capacity of preserving and utilizing the information encoded in frequency bands. Our proposed method significantly improves the efficiency of current classification method in specific dataset of the physionet. According to empirical evaluations, strong robustness and high decoding classification are two distinctive characteristics of our proposed work.