Brain-Computer Interfaces and Neural Engineering Laboratory, School of Computer Science and Electronic Engineering, University of Essex, Colchester, CO4 3SQ, UK.
Sci Rep. 2024 Nov 14;14(1):28003. doi: 10.1038/s41598-024-79309-y.
Neuroimaging studies have reported the possibility of semantic neural decoding to identify specific semantic concepts from neural activity. This offers promise for brain-computer interfaces (BCIs) for communication. However, translating these findings into a BCI paradigm has proven challenging. Existing EEG-based semantic decoding studies often rely on neural activity recorded when a cue is present, raising concerns about decoding reliability. To address this, we investigate the effects of cue presentation on EEG-based semantic decoding. In an experiment with a clear separation between cue presentation and mental task periods, we attempt to differentiate between semantic categories of animals and tools in four mental tasks. By using state-of-the-art decoding analyses, we demonstrate significant mean classification accuracies up to 71.3% during cue presentation but not during mental tasks, even with adapted analyses from previous studies. These findings highlight a potential issue when using neural activity recorded during cue presentation periods for semantic decoding. Additionally, our results show that semantic decoding without external cues may be more challenging than current state-of-the-art research suggests. By bringing attention to these issues, we aim to stimulate discussion and drive advancements in the field toward more effective semantic BCI applications.
神经影像学研究报告了从神经活动中识别特定语义概念的语义神经解码的可能性。这为脑机接口（BCI）的通信提供了可能。然而，将这些发现转化为 BCI 范式证明具有挑战性。现有的基于 EEG 的语义解码研究通常依赖于提示存在时记录的神经活动，这引发了对解码可靠性的担忧。为了解决这个问题，我们研究了提示呈现对基于 EEG 的语义解码的影响。在一个实验中，提示呈现和心理任务期间有明显的分离，我们试图在四个心理任务中区分动物和工具的语义类别。通过使用最先进的解码分析，我们在提示呈现期间证明了高达 71.3%的显著平均分类准确率，但在心理任务期间没有，即使使用了之前研究的适应性分析也是如此。这些发现强调了在使用提示呈现期间记录的神经活动进行语义解码时可能存在的问题。此外，我们的结果表明，没有外部提示的语义解码可能比当前最先进的研究所表明的更具挑战性。通过关注这些问题，我们旨在激发讨论并推动该领域朝着更有效的语义 BCI 应用方向发展。
