The increasing understanding of human brain functions makes it possible to directly interact with the brain for therapeutic purposes. Implantable brain computer interfaces promise to replace or restore motor functions in patients with partial or complete paralysis. We postulate that neuronal states associated with gestures, as they are used in the finger spelling alphabet of sign languages, provide an excellent signal for implantable brain computer interfaces to restore communication. To test this, we evaluated decodability of four gestures using high-density electrocorticography in two participants. The electrode grids were located subdurally on the hand knob area of the sensorimotor cortex covering a surface of 2.5–5.2 cm2. Using a pattern-matching classification approach four types of hand gestures were classified based on their pattern of neuronal activity. In the two participants the gestures were classified with 97 and 74 % accuracy. The high frequencies (>65 Hz) allowed for the best classification results. This proof-of-principle study indicates that the four gestures are associated with a reliable and discriminable spatial representation on a confined area of the sensorimotor cortex. This robust representation on a small area makes hand gestures an interesting control feature for an implantable BCI to restore communication for severely paralyzed people.
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机译：人们对人脑功能的日益了解，使其有可能与脑直接相互作用以达到治疗目的。植入式脑计算机接口有望在部分或完全瘫痪的患者中替代或恢复运动功能。我们假定与手势相关的神经元状态，如手势语言的手指拼写字母中所使用的，为植入式大脑计算机接口恢复通信提供了极好的信号。为了测试这一点，我们在两名参与者中使用高密度脑电图评估了四个手势的可解码性。电极网格位于感觉运动皮层的把手区域的硬膜下，覆盖2.5-5.2 cm 2 的表面。使用模式匹配分类方法，根据手势的神经元活动模式将其分为四种类型。在两个参与者中，手势的分类精度分别为97％和74％。高频（> 65 Hz）可获得最佳分类结果。这项原理证明研究表明，这四个手势与在感觉运动皮层的受限区域上的可靠且可区分的空间表示相关联。这种在小区域上的稳健表现使手势成为可植入BCI的有趣控制功能，可恢复严重瘫痪者的交流。
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