This study suggests a new approach to EEG data classification by exploring
the idea of using evolutionary computation to both select useful discriminative
EEG features and optimise the topology of Artificial Neural Networks. An
evolutionary algorithm is applied to select the most informative features from
an initial set of 2550 EEG statistical features. Optimisation of a Multilayer
Perceptron (MLP) is performed with an evolutionary approach before
classification to estimate the best hyperparameters of the network. Deep
learning and tuning with Long Short-Term Memory (LSTM) are also explored, and
Adaptive Boosting of the two types of models is tested for each problem. Three
experiments are provided for comparison using different classifiers: one for
attention state classification, one for emotional sentiment classification, and
a third experiment in which the goal is to guess the number a subject is
thinking of. The obtained results show that an Adaptive Boosted LSTM can
achieve an accuracy of 84.44%, 97.06%, and 9.94% on the attentional, emotional,
and number datasets, respectively. An evolutionary-optimised MLP achieves
results close to the Adaptive Boosted LSTM for the two first experiments and
significantly higher for the number-guessing experiment with an Adaptive
Boosted DEvo MLP reaching 31.35%, while being significantly quicker to train
and classify. In particular, the accuracy of the nonboosted DEvo MLP was of
79.81%, 96.11%, and 27.07% in the same benchmarks. Two datasets for the
experiments were gathered using a Muse EEG headband with four electrodes
corresponding to TP9, AF7, AF8, and TP10 locations of the international EEG
placement standard. The EEG MindBigData digits dataset was gathered from the
TP9, FP1, FP2, and TP10 locations.

本研究提出一种使用进化计算来选择有用的识别脑电特征并优化人工神经网络拓扑结构的新方法，同时探讨了深度学习和长短时记忆的调整，使用三个分类器对注意状态、情感和数字数据集进行比较，结果表明自适应提升的 LSTM 可以在注意力、情感和数字数据集上分别达到 84.44%、97.06% 和 9.94% 的准确度，进化优化的 MLP 趋近于自适应提升的 LSTM。