The interaction of humans and robots (HRI) is of great relevance for the field of neurorobotics as it can provide insights on motor control and sensor processing mechanisms in humans that can be applied to robotics. We propose a spiking neural network (SNN) to trigger motion reflexes on a robotic hand based on human EMG data. The first part of the network takes EMG signals to measure muscle activity, then classify the data to detect which finger is active in the human hand. The second part triggers single finger reflexes using the classification output. The finger reflexes are modeled with motion primitives activated with an oscillator and mapped to the robot kinematic. We evaluated the SNN by having users wear a non-invasive EMG sensor, record a training dataset, and then flex different fingers, one at a time. The muscle activity was recorded using a Myo sensor with eight channels. EMG signals were successfully encoded into spikes as input for the SNN. The classification could detect the active finger to trigger motion generation of finger reflexes. The SNN was able to control a real Schunk SVH robotic hand. Being able to map myo-electric activity to functions of motor control for a task, can provide an interesting interface for robotic applications, and also to study brain functioning. SNN provide a challenging but interesting framework to interact with human data. In future work the approach will be extended to control a robot arm at the same time.