标题： 一种结合信道方法解码颅内 EEG 信号：通过空间信息整合提高准确性 显示英文标题

标题： A Combined Channel Approach for Decoding Intracranial EEG Signals: Enhancing Accuracy through Spatial Information Integration

摘要： 颅内脑电图（iEEG）记录具有高空间和时间分辨率以及优越的信噪比（SNR），使开发用于神经解码的精确脑机接口（BCI）系统成为可能。 然而，该过程的侵入性显著限制了iEEG数据集在参与人数和记录会话时长方面的可用性。 为解决这一限制，我们提出了一种针对iEEG信号解码优化的单参与者机器学习模型。 该模型采用18个关键特征，并在两种模式下运行：最佳通道模式和组合通道模式。 组合通道模式整合了多个脑区的空间信息，从而实现了更优的分类性能。 在三个数据集——音乐重建、视听和AJILE12——上的评估表明，组合通道模式在所有分类器上始终优于最佳通道模式。 在表现最佳的情况下，随机森林在音乐重建数据集中获得了0.81 +/- 0.05的F1分数，在视听数据集中获得了0.82 +/- 0.10的F1分数，而XGBoost在AJILE12数据集中获得了0.84 +/- 0.08的F1分数。 此外，对组合通道模式中脑区贡献的分析表明，该模型识别出与每个任务的生理预期一致的相关脑区，并有效结合这些区域中的电极数据以实现高性能。 这些发现突显了整合跨脑区空间信息以提高任务解码的潜力，为推进BCI系统和神经技术应用提供了新途径。 显示英文摘要

摘要： Intracranial EEG (iEEG) recording, characterized by high spatial and temporal resolution and superior signal-to-noise ratio (SNR), enables the development of precise brain-computer interface (BCI) systems for neural decoding. However, the invasive nature of the procedure significantly limits the availability of iEEG datasets in terms of both the number of participants and the duration of recorded sessions. To address this limitation, we propose a single-participant machine learning model optimized for decoding iEEG signals. The model employs 18 key features and operates in two modes: best channel and combined channel. The combined channel mode integrates spatial information from multiple brain regions, leading to superior classification performance. Evaluations across three datasets -- Music Reconstruction, Audio Visual, and AJILE12 -- demonstrate that the combined channel mode consistently outperforms the best channel mode across all classifiers. In the best-performing cases, Random Forest achieved an F1 score of 0.81 +/- 0.05 in the Music Reconstruction dataset and 0.82 +/- 0.10 in the Audio Visual dataset, while XGBoost achieved an F1 score of 0.84 +/- 0.08 in the AJILE12 dataset. Furthermore, the analysis of brain region contributions in the combined channel mode revealed that the model identifies relevant brain regions aligned with physiological expectations for each task and effectively combines data from electrodes in these regions to achieve high performance. These findings highlight the potential of integrating spatial information across brain regions to improve task decoding, offering new avenues for advancing BCI systems and neurotechnological applications.