Moly Alexandre, Aksenov Alexandre, Martel Félix, Aksenova Tetiana
Université Grenoble Alpes, CEA, LETI, Clinatec, Grenoble, France.
Independent Researcher, Montpellier, France.
Front Hum Neurosci. 2023 Mar 6;17:1075666. doi: 10.3389/fnhum.2023.1075666. eCollection 2023.
Motor Brain-Computer Interfaces (BCIs) create new communication pathways between the brain and external effectors for patients with severe motor impairments. Control of complex effectors such as robotic arms or exoskeletons is generally based on the real-time decoding of high-resolution neural signals. However, high-dimensional and noisy brain signals pose challenges, such as limitations in the generalization ability of the decoding model and increased computational demands.
The use of sparse decoders may offer a way to address these challenges. A sparsity-promoting penalization is a common approach to obtaining a sparse solution. BCI features are naturally structured and grouped according to spatial (electrodes), frequency, and temporal dimensions. Applying group-wise sparsity, where the coefficients of a group are set to zero simultaneously, has the potential to decrease computational time and memory usage, as well as simplify data transfer. Additionally, online closed-loop decoder adaptation (CLDA) is known to be an efficient procedure for BCI decoder training, taking into account neuronal feedback. In this study, we propose a new algorithm for online closed-loop training of group-wise sparse multilinear decoders using -Penalized Recursive Exponentially Weighted N-way Partial Least Square (PREW-NPLS). Three types of sparsity-promoting penalization were explored using = 0., 0.5, 1.
The algorithms were tested offline in a pseudo-online manner for features grouped by spatial dimension. A comparison study was conducted using an epidural ECoG dataset recorded from a tetraplegic individual during long-term BCI experiments for controlling a virtual avatar (left/right-hand 3D translation). Novel algorithms showed comparable or better decoding performance than conventional REW-NPLS, which was achieved with sparse models. The proposed algorithms are compatible with real-time CLDA.
The proposed algorithm demonstrated good performance while drastically reducing the computational load and the memory consumption. However, the current study is limited to offline computation on data recorded with a single patient, with penalization restricted to the spatial domain only.
运动脑机接口（BCI）为严重运动障碍患者在大脑与外部效应器之间创建了新的通信途径。对诸如机器人手臂或外骨骼等复杂效应器的控制通常基于高分辨率神经信号的实时解码。然而，高维和有噪声的脑信号带来了挑战，例如解码模型泛化能力的局限性以及计算需求的增加。
使用稀疏解码器可能提供一种应对这些挑战的方法。促进稀疏性的惩罚是获得稀疏解的常用方法。BCI特征根据空间（电极）、频率和时间维度自然地结构化和分组。应用组稀疏性，即一组的系数同时设置为零，有可能减少计算时间和内存使用，以及简化数据传输。此外，在线闭环解码器自适应（CLDA）已知是一种用于BCI解码器训练的有效程序，它考虑了神经元反馈。在本研究中，我们提出了一种新算法，用于使用 - 惩罚递归指数加权N路偏最小二乘（PREW-NPLS）对组稀疏多线性解码器进行在线闭环训练。使用 = 0.、0.5、1探索了三种促进稀疏性的惩罚类型。
以伪在线方式对按空间维度分组的特征在离线状态下测试了这些算法。使用从一名四肢瘫痪个体在长期BCI实验期间记录的硬膜外ECoG数据集进行了一项比较研究，用于控制虚拟化身（左手/右手3D平移）。新算法显示出与传统REW-NPLS相当或更好的解码性能，这是通过稀疏模型实现的。所提出的算法与实时CLDA兼容。
所提出的算法在大幅降低计算负荷和内存消耗的同时表现出良好的性能。然而，当前研究仅限于对单个患者记录的数据进行离线计算，惩罚仅限制在空间域。
