Brain Signal Processing And Analysis Based On Deep Learning

With the rapid development of artificial intelligence and human-computer interaction technologies,brain signal processing technology has been more and more widely used in recent years.Brain signals are generated by neuronal activity in the brain,reflect brain activity,and are an essential channel to uncover the mysteries of the brain.Brain signals have been widely used in many fields,especially in the field of brain disease diagnosis.Brain signals can be divided into SEEG and EEG according to other collection methods.EEG is a kind of brain signal data collected through non-invasive collection.Because of the advantages of non-invasive collection,it is widely used in insomnia diagnosis,sleep staging,etc.SEEG is a kind of brain signal data collected invasively.Still,it can collect brain information of different brain areas in the three-dimensional space of the brain,so SEEG is also often used in research in the fields of epilepsy diagnosis,brain function area positioning,etc.Insomnia prediction and epilepsy diagnosis are the two most widely used scenarios of EEG signals.For example,many scholars have proposed methods based on traditional signal processing,machine learning,or neural networks in diagnosing insom-nia.These methods are widely used in data mining of insomnia EEG signals.Traditional signal processing methods often require complex data preprocessing,and it is difficult to characterize the EEG characteristics of insomnia.The generalization ability of the model based on machine learning and deep learning is weaker when the dataset is small.At the same time,in epilepsy diagnosis,SEEG signals can help doctors complete tasks such as epilepsy clinical diagnosis and research and have significant data mining value.However,there has been less work on SEEG data mining based on deep learning methods in recent years.This article will start from the EEG and SEEG data,respectively,and use machine learning and deep learning methods in insomnia prediction and epilepsy warning scenarios to conduct in-depth research on brain signal processing and analysis.First of all,this article uses the method of machine learning to analyze the characteristics of the EEG signals of patients with insomnia and completes the task of predicting insomnia based on the EEG? On this basis,further studies on the recognition of epilepsy features based on SEEG signals and proposes Three-dimensional SEEG classification model based on few shot learning? Finally,this paper proposes an epilepsy SEEG classification model based on domain adversarial learning,which realizes the function of early warning and auxiliary diagnosis of epilepsy.The main contributions of this paper are summarized as follows:· Insomnia prediction model based on KNN optimization algorithm.This paper proposes a EEG classification model based on the KNN optimization algorithm.In insomnia prediction,this method is based on the traditional EEG signal processing method by encoding the characteristic waveforms appearing in the EEG data of patients with insomnia.The model can learn an effective high-level representation of EEG signals and then use the optimized KNN algorithm to achieve insomnia prediction.Experiments on two natural insomnia scalp EEG data sets show that our method is about 5% more accurate than the benchmark method?· A SEEG classification model based on few shot learning.The SEEG classifi-cation model based on a few shot learning proposed in this paper is the first to use deep understanding to conduct data mining on SEEG data,which has positive signif-icance for the early detection of epilepsy.The model uses variational self-encoding to extract each SEEG signal's characteristics while also reducing noise interference.In addition,by constructing multiple task sets through random sampling and using the parameter initialization strategy of meta-learning,the model can improve the generalization ability to recognize SEEG signals?· A SEEG classification model based on domain adversarial learning.This pa-per proposes an SEEG classification model based on domain adversarial learning.They consider that the model will be interfered with by individual SEEG informa-tion during encoding,making the prediction results from unstable and poor perfor-mance.The model is based on the idea of adversarial learning and consists of an encoder,classifier,and domain discriminator.Through the adversarial learning of the encoder and the domain discriminator,the classifier can automatically learn the target SEEG features under the interference of the individual SEEG noise and ac-curately identify the SEEG signal.This method further improves the stability and performance of the model in the early detection of epilepsy.