A major shortcoming of medical practice is the lack of an objective measure of conscious level. Impairment of consciousness is common, e.g. following brain injury and seizures, which can also interfere with sensory processing and volitional responses. This is also an important pitfall in neurophysiological methods that infer awareness via command following, e.g. using functional MRI or electroencephalography (EEG). Transcranial electrical stimulation (TES) can be employed to non-invasively stimulate the brain, bypassing sensory inputs, and has already showed promising results in providing reliable indicators of brain state. However, current non-invasive solutions have been limited to magnetic stimulation, which is not easily translatable to clinical settings. Our long-term vision is to develop an objective measure of brain state that can be used at the bedside, without requiring patients to understand commands or initiate motor responses. In this study, we demonstrated the feasibility of a framework using Deep Learning algorithms to classify EEG brain responses evoked by a defined multi-dimensional pattern of TES. We collected EEG-TES data from 11 participants and found that delivering transcranial direct current stimulation (tDCS) to posterior cortical areas targeting the angular gyrus elicited an exceptionally reliable brain response. For this paradigm, our best Convolutional Neural Network model reached a 92% classification F1-score on Holdout data from participants never seen during training, significantly surpassing human-level performance at 60-70% accuracy. These findings establish a framework for robust consciousness measurement for clinical use. In this spirit, we documented and open-sourced our datasets and codebase in full, to be used freely by the neuroscience and AI research communities, who may replicate our results with free tools like GitHub, Kaggle, and Colab.

翻译：医疗实践中的一个主要缺陷是缺乏对意识水平的客观测量。意识障碍是常见的，例如在脑损伤和癫痫发作后，这也会干扰感觉处理和意志反应。这也是神经生理学方法中的一个重要缺陷，这些方法通过指令跟随来推断意识，例如使用功能磁共振成像或脑电图（EEG）。经颅电刺激（TES）可用于无创地刺激大脑，绕过感觉输入，并且在提供可靠的大脑状态指标方面已经显示出有希望的结果。然而，当前的无创解决方案仅限于磁刺激，这不易转化为临床环境。我们的长期愿景是开发一种可在床边使用的、无需患者理解指令或启动运动反应的大脑状态客观测量方法。在本研究中，我们证明了一个框架的可行性，该框架使用深度学习算法对由定义的多维TES模式诱发的EEG大脑响应进行分类。我们收集了11名参与者的EEG-TES数据，发现向后部皮质区域（针对角回）施加经颅直流电刺激（tDCS）能引发异常可靠的大脑响应。对于此范式，我们最佳的卷积神经网络模型在训练期间从未见过的参与者的留出数据上达到了92%的分类F1分数，显著超过了人类水平的60-70%准确率。这些发现为临床应用的稳健意识测量建立了一个框架。本着这一精神，我们完整记录并开源了我们的数据集和代码库，供神经科学和人工智能研究社区自由使用，他们可以使用GitHub、Kaggle和Colab等免费工具复现我们的结果。