Romeni Simone, Toni Laura, Artoni Fiorenzo, Micera Silvestro
Department of Clinical Neurosciences, Faculty of Medicine, University of Geneva, Geneva, Switzerland.
APL Bioeng. 2024 Jun 12;8(2):026123. doi: 10.1063/5.0195680. eCollection 2024 Jun.
Electrical stimulation of the visual nervous system could improve the quality of life of patients affected by acquired blindness by restoring some visual sensations, but requires careful optimization of stimulation parameters to produce useful perceptions. Neural correlates of elicited perceptions could be used for fast automatic optimization, with electroencephalography as a natural choice as it can be acquired non-invasively. Nonetheless, its low signal-to-noise ratio may hinder discrimination of similar visual patterns, preventing its use in the optimization of electrical stimulation. Our work investigates for the first time the discriminability of the electroencephalographic responses to visual stimuli compatible with electrical stimulation, employing a newly acquired dataset whose stimuli encompass the concurrent variation of several features, while neuroscience research tends to study the neural correlates of single visual features. We then performed above-chance single-trial decoding of multiple features of our newly crafted visual stimuli using relatively simple machine learning algorithms. A decoding scheme employing the information from multiple stimulus presentations was implemented, substantially improving our decoding performance, suggesting that such methods should be used systematically in future applications. The significance of the present work relies in the determination of which visual features can be decoded from electroencephalographic responses to electrical stimulation-compatible stimuli and at which granularity they can be discriminated. Our methods pave the way to using electroencephalographic correlates to optimize electrical stimulation parameters, thus increasing the effectiveness of current visual neuroprostheses.
对视觉神经系统进行电刺激可以通过恢复一些视觉感觉来改善后天失明患者的生活质量，但需要仔细优化刺激参数以产生有用的感知。诱发感知的神经关联可用于快速自动优化，脑电图是自然的选择，因为它可以通过非侵入性方式获取。尽管如此，其低信噪比可能会妨碍对相似视觉模式的辨别，从而阻碍其在电刺激优化中的应用。我们的工作首次研究了与电刺激兼容的视觉刺激的脑电图反应的可辨别性，采用了一个新获取的数据集，其刺激包含多个特征的同时变化，而神经科学研究往往侧重于研究单个视觉特征的神经关联。然后，我们使用相对简单的机器学习算法对新设计的视觉刺激的多个特征进行了高于机会水平的单次试验解码。实施了一种利用来自多个刺激呈现信息的解码方案，显著提高了我们的解码性能，这表明此类方法应在未来应用中系统地使用。本工作的意义在于确定哪些视觉特征可以从与电刺激兼容的刺激的脑电图反应中解码出来，以及它们可以在何种粒度上被辨别。我们的方法为利用脑电图关联来优化电刺激参数铺平了道路，从而提高当前视觉神经假体的有效性。
