Adaptive Sequence-Based Stimulus Selection in an ERP-Based Brain-Computer Interface by Thompson Sampling in a Multi-Armed Bandit Problem.
Adaptive Sequence-Based Stimulus Selection in an ERP-Based Brain-Computer Interface by Thompson Sampling in a Multi-Armed Bandit Problem.
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DOI：
10.1109/bibm52615.2021.9669724
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发表时间：
2021-12
期刊：
影响因子：
--
通讯作者：
Kang, Jian
中科院分区：
文献类型：
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作者：
Ma, Tianwen;Huggins, Jane E;Kang, Jian
脑机接口（BCI）是一种解释大脑活动以帮助残疾人交流的设备。基于P300事件相关电位的脑机接口拼写器在屏幕上显示一系列事件，并在一系列非目标事件中搜索诱发的脑电图（EEG）数据以寻找目标P300事件相关电位（ERP）反应。黑板范式是一种常见的刺激呈现范式。虽然一些研究已经提出了数据驱动的刺激选择方法，他们遭受棘手的决策规则，大的计算复杂性，或错误传播的参与者谁表现不佳的静态范式。此外，没有一种方法直接应用于CB范式。在这项工作中，我们提出了一个基于序列的自适应刺激选择方法，使用汤普森采样多强盗问题的多个行动。在每个序列中，该算法随机选取一个固定大小的刺激子集，目的是识别所有的目标刺激，并通过减少不必要的非目标刺激来提高拼写速度。我们通过贝叶斯规则从原始分类器得分计算“干净”的刺激特定奖励。我们进行了广泛的模拟研究，比较我们的算法的静态CB范例。考虑到实际应用中的约束条件，我们证明了算法的鲁棒性。对于模拟数据与真实的数据最相似的场景，与静态CB范例相比，我们的算法的拼写效率提高了70%以上。
A Brain-Computer Interface (BCI) is a device that interprets brain activity to help people with disabilities communicate. The P300 ERP-based BCI speller displays a series of events on the screen and searches the elicited electroencephalogram (EEG) data for target P300 event-related potential (ERP) responses among a series of non-target events. The Checkerboard (CB) paradigm is a common stimulus presentation paradigm. Although a few studies have proposed data-driven methods for stimulus selection, they suffer from intractable decision rules, large computation complexity, or error propagation for participants who perform poorly under the static paradigm. In addition, none of the methods have been applied to the CB paradigm directly. In this work, we propose a sequence-based adaptive stimulus selection method using Thompson Sampling in the multi-bandit problem with multiple actions. During each sequence, the algorithm selects a random subset of stimuli with fixed size, aiming to identify all target stimuli and to improve the spelling speed by reducing the number of unnecessary non-target stimuli. We compute “clean” stimulus-specific rewards from raw classifier scores via the Bayes rule. We perform extensive simulation studies to compare our algorithm to the static CB paradigm. We show the robustness of our algorithm by considering the constraints of practical use. For scenarios where simulated data resemble the real data the most, the spelling efficiency of our algorithm increases by more than 70%, compared to the static CB paradigm.