Emotion Recognition Using a Reduced Set of EEG Channels Based on Holographic Feature Maps.

An important function of the construction of the Brain-Computer Interface (BCI) device is the development of a model that is able to recognize emotions from electroencephalogram (EEG) signals. Research in this area is very challenging because the EEG signal is non-stationary, non-linear, and contains a lot of noise due to artifacts caused by muscle activity and poor electrode contact. EEG signals are recorded with non-invasive wearable devices using a large number of electrodes, which increase the dimensionality and, thereby, also the computational complexity of EEG data.

Using Cutaneous Receptor Vibration to Uncover the Effect of Transcranial Magnetic Stimulation (TMS) on Motor Cortical Excitability.

BACKGROUND Little is known about how vibrational stimuli applied to hand digits affect motor cortical excitability. The present transcranial magnetic stimulation (TMS) study investigated motor evoked potentials (MEPs) in the upper extremity muscle following high-frequency vibratory digit stimulation. MATERIAL AND METHODS High-frequency vibration was applied to the upper extremity digit II utilizing a miniature electromagnetic solenoid-type stimulator-tactor in 11 healthy study participants.

Cochlea-inspired speech recognition interface.

Automatic speech recognition (ASR) technology provides a natural interface for human-machine interaction. Typical ASR systems can achieve high performance in quiet environments but, unlike humans, perform poorly in real-world situations. To better simulate the human auditory periphery and improve the performance in realistic noisy scenarios, we propose two models of speech recognition front-ends based on a biophysical cochlear model.