In this paper, a general overview regarding neural recording, classical signal processing techniques and machine learning classification algorithms applied to monitor brain activity is presented. Currently, several approaches classified as electrical, magnetic, neuroimaging recordings and brain stimulations are available to obtain neural activity of the human brain. Among them, non-invasive methods like electroencephalography (EEG) are commonly employed, as they can provide a high degree of temporal resolution (on the order of milliseconds) and acceptable space resolution. In addition, it is simple, quick, and does not create any physical harm or stress to patients. Concerning signal processing, once the neural signals are acquired, different procedures can be applied for feature extraction. In particular, brain signals are normally processed in time, frequency, and/or space domains. The features extracted are then used for signal classification depending on its characteristics such us the mean, variance or band power. The role of machine learning in this regard has become of key importance during the last years due to its high capacity to analyze complex amounts of data. The algorithms employed are generally classified in supervised, unsupervised and reinforcement techniques. A deep review of the most used machine learning algorithms and the advantages/drawbacks of most used methods is presented. Finally, a study of these procedures utilized in a very specific and novel research field of electroencephalography, i.e., autobiographical memory deficits in schizophrenia, is outlined.

Purpose In this paper, a new automated procedure based on deep learning methods for schizophrenia diagnosis is presented. Methods To this aim, electroencephalogram signals obtained using a 32-channel helmet are prominently used to analyze high temporal resolution information from the brain. By these means, the data collected is employed to evaluate the class likelihoods using a neuronal network based on radial basis functions and a fuzzy means algorithm. ResultsThe results obtained with real datasets validate the high accuracy of the proposed classification method. Thus, effectively characterizing the changes in EEG signals acquired from schizophrenia patients and healthy volunteers. More specifically, values of accuracy better than 93% has been obtained in the present research. Additionally, a comparative study with other approaches based on well-knows machine learning methods shows that the proposed method provides better results than recently proposed algorithms in schizophrenia detection. ConclusionThe proposed method can be used as a diagnostic tool in the detection of the schizophrenia, helping for early diagnosis and treatment.

Presently, several million people suffer from major depressive and bipolar disorders. Thus, the modelling, characterization, classification, diagnosis, and analysis of such mental disorders bears great significance in medical research. Electroencephalogram records provide important information to improve clinical diagnosis and are very useful in the scientific community. In this work, electroencephalogram records and patient data from the Hospital Virgen de la Luz in Cuenca (Spain) were processed for a correct classification of bipolar disorders. This work implemented an innovative radial basis function-based neural network employing a fuzzy means algorithm. The results show that the proposed method is an effective approach for discrimination of two kinds of classes, i.e., bipolar disorder patients and healthy persons. The proposed algorithm achieved the best performance compared with other machine learning techniques such as Bayesian linear discriminant analysis, Gaussian naive Bayes, decision trees, K-nearest neighbour, or support vector machine, showing a very high accuracy close to 97%. Therefore, the neural network technique presented could be used as a new tool for the diagnosis of bipolar disorder, considering the possibility of integrating this method into medical software.

Electroencephalogram is a useful interface system that translates the human electrical brain activity into voltage signals. By these means, the recorded brain waves can be employed to characterize, classify, or diagnose mental disorders. A novel neural network model to classify patients with schizophrenia based on electroencephalograms is presented. The proposed model decomposes the multichannel electroencephalogram records into a group of multivariate novel radial basis functions using a fuzzy means algorithm. The decomposition permits to extract different electroencephalogram channel information and distinguish between two sort of classes i.e., schizophrenic patients and healthy controls. Results show improved accuracy compared to classical algorithms reported in the literature i.e., Support Vector Machine, Bayesian Linear Discriminant Analysis, Gaussian Naive Bayes, K-Nearest Neighbour or Adaboost. As a result, the method presented in this paper achieves the highest balanced accuracy, recall, precision and F1 score values, close to 93 % in all cases. The model presented in this paper may be integrated in real time tools involved during the diagnostic of schizophrenia.

Electroencephalogram is a useful interface system that translates the human electrical brain activity into voltage signals. By these means, the recorded brain waves can be employed to characterize, classify, or diagnose mental disorders. A novel neural network model to classify patients with schizophrenia based on electroencephalograms is presented. The proposed model decomposes the multichannel electroencephalogram records into a group of multivariate novel radial basis functions using a fuzzy means algorithm. The decomposition permits to extract different electroencephalogram channel information and distinguish between two sort of classes i.e., schizophrenic patients and healthy controls. Results show improved accuracy compared to classical algorithms reported in the literature i.e., Support Vector Machine, Bayesian Linear Discriminant Analysis, Gaussian Naive Bayes, K-Nearest Neighbour or Adaboost. As a result, the method presented in this paper achieves the highest balanced accuracy, recall, precision and F1 score values, close to 93 % in all cases. The model presented in this paper may be integrated in real time tools involved during the diagnostic of schizophrenia.