Dhindsa Kiret, Carcone Dean, Becker Suzanna
Neurotechnology and Neuroplasticity Lab, Department of Psychology, Neuroscience, and Behaviour, McMaster University, Hamilton, Ontario L8S 4L8, Canada
Neural Comput. 2017 Oct;29(10):2742-2768. doi: 10.1162/neco_a_01001. Epub 2017 Aug 4.
Brain-computer interfaces (BCIs) allow users to control a device by interpreting their brain activity. For simplicity, these devices are designed to be operated by purposefully modulating specific predetermined neurophysiological signals, such as the sensorimotor rhythm. However, the ability to modulate a given neurophysiological signal is highly variable across individuals, contributing to the inconsistent performance of BCIs for different users. These differences suggest that individuals who experience poor BCI performance with one class of brain signals might have good results with another. In order to take advantage of individual abilities as they relate to BCI control, we need to move beyond the current approaches. In this letter, we explore a new BCI design aimed at a more individualized and user-focused experience, which we call open-ended BCI. Individual users were given the freedom to discover their own mental strategies as opposed to being trained to modulate a given brain signal. They then underwent multiple coadaptive training sessions with the BCI. Our first open-ended BCI performed similarly to comparable BCIs while accommodating a wider variety of mental strategies without a priori knowledge of the specific brain signals any individual might use. Post hoc analysis revealed individual differences in terms of which sensory modality yielded optimal performance. We found a large and significant effect of individual differences in background training and expertise, such as in musical training, on BCI performance. Future research should be focused on finding more generalized solutions to user training and brain state decoding methods to fully utilize the abilities of different individuals in an open-ended BCI. Accounting for each individual's areas of expertise could have important implications on BCI training and BCI application design.
脑机接口（BCIs）允许用户通过解读其大脑活动来控制设备。为简单起见，这些设备被设计为通过有目的地调节特定的预定神经生理信号（如感觉运动节律）来操作。然而，调节给定神经生理信号的能力在个体之间差异很大，这导致了脑机接口对不同用户的性能不一致。这些差异表明，在一类脑信号上脑机接口性能不佳的个体，在另一类脑信号上可能会有好的结果。为了利用与脑机接口控制相关的个体能力，我们需要超越当前的方法。在这封信中，我们探索了一种新的脑机接口设计，旨在提供更个性化和以用户为中心的体验，我们称之为开放式脑机接口。个体用户被给予自由去发现他们自己的心理策略，而不是被训练去调节给定的脑信号。然后他们与脑机接口进行了多次协同自适应训练。我们的第一个开放式脑机接口在容纳更广泛的心理策略方面表现得与同类脑机接口相似，且无需事先了解任何个体可能使用的特定脑信号。事后分析揭示了在产生最佳性能的感觉模态方面的个体差异。我们发现背景训练和专业知识（如音乐训练）方面的个体差异对脑机接口性能有很大且显著的影响。未来的研究应专注于找到更通用的用户训练和脑状态解码方法的解决方案，以在开放式脑机接口中充分利用不同个体的能力。考虑每个个体的专业领域可能对脑机接口训练和脑机接口应用设计有重要影响。
