Research Group in Electronic, Biomedical and Telecommunication Engineering, Facultad de Comunicación, University of Castilla-La Mancha, 16071 Cuenca, Spain.
Instituto de Tecnologías Audiovisuales de Castilla-La Mancha, University of Castilla-La Mancha, 16071 Cuenca, Spain.
Sensors (Basel). 2021 Apr 27;21(9):3050. doi: 10.3390/s21093050.
In recent years, electroencephalographic (EEG) signals have been intensively used in the area of emotion recognition, partcularly in distress identification due to its negative impact on physical and mental health. Traditionally, brain activity has been studied from a frequency perspective by computing the power spectral density of the EEG recordings and extracting features from different frequency sub-bands. However, these features are often individually extracted from single EEG channels, such that each brain region is separately evaluated, even when it has been corroborated that mental processes are based on the coordination of different brain areas working simultaneously. To take advantage of the brain's behaviour as a synchronized network, in the present work, 2-D and 3-D spectral images constructed from common 32 channel EEG signals are evaluated for the first time to discern between emotional states of calm and distress using a well-known deep-learning algorithm, such as AlexNet. The obtained results revealed a significant improvement in the classification performance regarding previous works, reaching an accuracy about 84%. Moreover, no significant differences between the results provided by the diverse approaches considered to reconstruct 2-D and 3-D spectral maps from the original location of the EEG channels over the scalp were noticed, thus suggesting that these kinds of images preserve original spatial brain information.
近年来，脑电图（EEG）信号在情绪识别领域得到了广泛应用，特别是在因对身心健康产生负面影响而导致的痛苦识别方面。传统上，通过计算 EEG 记录的功率谱密度并从不同的频带子带中提取特征，从频率的角度研究大脑活动。然而，这些特征通常是从单个 EEG 通道中单独提取的，因此，即使已经证实心理过程是基于同时工作的不同大脑区域的协调，每个大脑区域也会被分别评估。为了利用大脑作为同步网络的行为，在目前的工作中，首次评估了从常见的 32 通道 EEG 信号构建的 2-D 和 3-D 谱图像，以便使用众所周知的深度学习算法（如 AlexNet）来区分平静和痛苦的情绪状态。获得的结果显示，与之前的工作相比，分类性能有了显著提高，准确率约为 84%。此外，从头皮上 EEG 通道的原始位置重建 2-D 和 3-D 谱图的不同方法提供的结果之间没有注意到显著差异，这表明这些图像保留了原始的空间大脑信息。
