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. It also reduces the level of comfort of the subjects. This paper implements our holographic features, investigates electrode selection, and uses the most relevant channels to maximize model accuracy. The ReliefF and Neighborhood Component Analysis (NCA) methods were used to select the optimal electrodes. Verification was performed on four publicly available datasets. Our holographic feature maps were constructed using computer-generated holography (CGH) based on the values of signal characteristics displayed in space. The resulting 2D maps are the input to the Convolutional Neural Network (CNN), which serves as a feature extraction method. This methodology uses a reduced set of electrodes, which are different between men and women, and obtains state-of-the-art results in a three-dimensional emotional space. The experimental results show that the channel selection methods improve emotion recognition rates significantly with an accuracy of 90.76% for valence, 92.92% for arousal, and 92.97% for dominance.

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. The conditioning stimulation (C) preceded the test magnetic stimulation (T) by inter-stimulus intervals (ISIs) of 5-500 ms in 2 experimental sessions. The TMS was applied over the primary motor cortex for the hand abductor pollicis-brevis (APB) muscle. RESULTS Dunnett's multiple comparisons test indicated significant suppression of MEP amplitudes at ISIs of 200 ms (P=0.001), 300 ms (P=0.023), and 400 ms (P=0.029) compared to control. CONCLUSIONS MEP amplitude suppression was observed in the APB muscle at ISIs of 200-400 ms, applying afferent signaling that originates in skin receptors following the vibratory stimuli. The study provides novel insight on the time course and MEP modulation following cutaneous receptor vibration of the hand digit. The results of the study may have implications in neurology in the neurorehabilitation of patients with increased amplitude of MEPs.

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. The first front-end is based on the method of signal reconstruction from a basilar membrane response. When applied to noisy speech, this method results in improved signal quality. This method can be used as a preprocessing step in a standard ASR system and can also be used as a noise reduction technique for other applications. The second front-end we propose is based on the construction of speech recognition coefficients directly from a basilar membrane response. Experimental results using a continuous-density hidden Markov model (HMM) recognizer demonstrate significant improvement in performance compared to standard Mel-frequency cepstral coefficients (MFCC) in various types of noisy conditions. Graphical Abstract Speech recognition model based on cochlear front-end.