Current prostheses are limited in their ability to provide direct sensory feedback to users with missing limb. Several efforts have been made to restore tactile sensation to amputees but the somatotopic tactile feedback often results in unnatural sensations, and it is yet unclear how and what information the somatosensory system receives during voluntary movement. The present study proposes an efficient model of stacked sparse autoencoder and back propagation neural network for detecting sensory events from a highly flexible electrocorticography (ECoG) electrode. During the mechanical stimulation with Von Frey (VF) filament on the plantar surface of rats’ foot, simultaneous recordings of tactile afferent signals were obtained from primary somatosensory cortex (S1) in the brain. In order to achieve a model with optimal performance, Particle Swarm Optimization and Adaptive Moment Estimation (Adam) were adopted to select the appropriate number of neurons, hidden layers and learning rate of each sparse auto-encoder. We evaluated the stimulus-evoked sensation by using an automated up-down (UD) method otherwise called UDReader. The assessment of tactile thresholds with VF shows that the right side of the hind-paw was significantly more sensitive at the tibia-(p = 6.50 × 10⁻⁴), followed by the saphenous-(p = 7.84 × 10⁻⁴), and sural-(p = 8.24 × 10⁻⁴). We then validated our proposed model by comparing with the state-of-the-art methods, and recorded accuracy of 98.8%, sensitivity of 96.8%, and specificity of 99.1%. Hence, we demonstrated the effectiveness of our algorithms in detecting sensory events through flexible ECoG recordings which could be a viable option in restoring somatosensory feedback.

当前的假肢无​​法为肢体缺失的用户提供直接的感官反馈。已经做出了一些努力来恢复截肢者的触觉，但体感触觉反馈通常会导致不自然的感觉，目前尚不清楚体感系统在随意运动过程中如何接收信息以及接收哪些信息。本研究提出了一种有效的堆叠稀疏自编码器和反向传播神经网络模型，用于检测来自高度灵活的脑电图 (ECoG) 电极的感觉事件。在用 Von Frey (VF) 灯丝对大鼠足底表面进行机械刺激期间，从大脑中的初级体感皮层 (S1) 获得触觉传入信号的同步记录。为了获得性能最优的模型，采用粒子群优化和自适应矩估计（Adam）来选择适当数量的神经元、隐藏层和每个稀疏自动编码器的学习率。我们使用自动上下 (UD) 方法评估刺激诱发的感觉，也称为 UDReader。用 VF 评估触觉阈值表明后爪右侧在胫骨处明显更敏感 - (p  = 6.50 × 10 ^-4 )，然后是隐静脉-( p  = 7.84 × 10 ^-4 ) 和腓肠-( p  = 8.24 × 10 ^-4 )。然后，我们通过与最先进的方法进行比较来验证我们提出的模型，并记录了 98.8% 的准确度、96.8% 的灵敏度和 99.1% 的特异性。因此，我们通过灵活的 ECoG 记录证明了我们的算法在检测感觉事件方面的有效性，这可能是恢复体感反馈的可行选择。