In this paper, two versatile deep learning methods are proposed for the efficient classification of epilepsy and schizophrenia from EEG datasets. The main advantage of using deep learning when compared to other machine learning algorithms is that it has the capability to accomplish feature engineering on its own. The first method proposed is a Sparse Autoencoder (SAE) with swarm based deep learning method and it is named as (SASDL) using Particle Swarm Optimization (PSO) technique, Cuckoo Search Optimization (CSO) technique and Bat Algorithm (BA) technique; and the second technique proposed is the Reinforcement Learning based on Bidirectional Long-Short Term Memory (BiLSTM), Attention Mechanism, Tree LSTM and Q learning, and it is named as (RBATQ) technique.

中文翻译：

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SASDL 和 RBATQ：稀疏自动编码器，采用基于群的深度学习和基于强化的 Q 学习，用于脑电图分类


本文提出了两种通用的深度学习方法，用于根据脑电图数据集对癫痫和精神分裂症进行有效分类。与其他机器学习算法相比，使用深度学习的主要优点是它能够自行完成特征工程。提出的第一种方法是基于群体深度学习方法的稀疏自动编码器（SAE），它被命名为（SASDL），使用粒子群优化（PSO）技术、布谷鸟搜索优化（CSO）技术和蝙蝠算法（BA）技术；提出的第二种技术是基于双向长短期记忆（BiLSTM）、注意力机制、树LSTM和Q学习的强化学习，被命名为（RBATQ）技术。