Gonzalez-Navarro Paula, Celik Basak, Moghadamfalahi Mohammad, Akcakaya Murat, Fried-Oken Melanie, Erdoğmuş Deniz
Cognitive Systems Laboratory, Northeastern University, Boston, MA, United States.
CAMBI (Consortium for Accessible Multimodal Brain-Body Interfaces), Portland, OR, United States.
Front Hum Neurosci. 2022 Jan 25;15:788258. doi: 10.3389/fnhum.2021.788258. eCollection 2021.
Error related potentials (ErrP), which are elicited in the EEG in response to a perceived error, have been used for error correction and adaption in the event related potential (ERP)-based brain computer interfaces designed for typing. In these typing interfaces, ERP evidence is collected in response to a sequence of stimuli presented usually in the visual form and the intended user stimulus is probabilistically inferred (stimulus with highest probability) and presented to the user as the decision. If the inferred stimulus is incorrect, ErrP is expected to be elicited in the EEG. Early approaches to use ErrP in the design of typing interfaces attempt to make hard decisions on the perceived error such that the perceived error is corrected and either the sequence of stimuli are repeated to obtain further ERP evidence, or without further repetition the stimulus with the second highest probability is presented to the user as the decision of the system. Moreover, none of the existing approaches use a language model to increase the performance of typing. In this work, unlike the existing approaches, we study the potential benefits of fusing feedback related potentials (FRP), a form of ErrP, with ERP and context information (language model, LM) in a Bayesian fashion to detect the user intent. We present experimental results based on data from 12 healthy participants using RSVP Keyboard™ to complete a copy-phrase-task. Three paradigms are compared: [P1] uses only ERP/LM Bayesian fusion; [P2] each RSVP sequence is appended with the top candidate in the alphabet according to posterior after ERP evidence fusion; corresponding FRP is then incorporated; and [P3] the top candidate is shown as a prospect to generate FRP evidence only if its posterior exceeds a threshold. Analyses indicate that ERP/LM/FRP evidence fusion during decision making yields significant speed-accuracy benefits for the user.
错误相关电位（ErrP）是在脑电图中因感知到错误而引发的，已被用于基于事件相关电位（ERP）的打字脑机接口中的错误纠正和适应。在这些打字接口中，通常以视觉形式呈现一系列刺激来收集ERP证据，并概率性地推断（概率最高的刺激）预期的用户刺激，并将其作为决策呈现给用户。如果推断的刺激不正确，预计脑电图中会引发ErrP。早期在打字接口设计中使用ErrP的方法试图对感知到的错误做出硬性决策，以便纠正感知到的错误，要么重复刺激序列以获得更多ERP证据，要么不进行进一步重复就将概率第二高的刺激作为系统决策呈现给用户。此外，现有的方法都没有使用语言模型来提高打字性能。在这项工作中，与现有方法不同，我们研究了以贝叶斯方式将反馈相关电位（FRP，ErrP的一种形式）与ERP和上下文信息（语言模型，LM）融合以检测用户意图的潜在好处。我们展示了基于12名健康参与者使用RSVP Keyboard™完成复制短语任务的数据的实验结果。比较了三种范式：[P1]仅使用ERP/LM贝叶斯融合；[P2]根据ERP证据融合后的后验概率，在每个RSVP序列后附加字母表中的顶级候选词；然后纳入相应的FRP；[P3]仅当顶级候选词的后验概率超过阈值时，才将其作为预期选项显示以生成FRP证据。分析表明，决策过程中的ERP/LM/FRP证据融合为用户带来了显著的速度-准确性优势。
