The spatio-temporal architecture of everyday manual behavior.

In everyday activities, humans move alike to manipulate objects. Prior works suggest that hand movements are built by a limited set of basic building blocks consisting of a set of common postures. However, how the low dimensionality of hand movements supports the adaptability and flexibility of natural behavior is unknown. Through a sensorized glove, we collected kinematics data from thirty-six participants preparing and having breakfast in naturalistic conditions. By means of an unbiased analysis, we identified a repertoire of hand states. Then, we tracked their transitions over time. We found that manual behavior can be described in space through a complex organization of basic configurations. These, even in an unconstrained experiment, recurred across subjects. A specific temporal structure, highly consistent within the sample, seems to integrate such identified hand shapes to realize skilled movements. These findings suggest that the simplification of the motor commands unravels in the temporal dimension more than in the spatial one.

Inkjet-printed fully customizable and low-cost electrodes matrix for gesture recognition.

The use of surface electromyography (sEMG) is rapidly spreading, from robotic prostheses and muscle computer interfaces to rehabilitation devices controlled by residual muscular activities. In this context, sEMG-based gesture recognition plays an enabling role in controlling prosthetics and devices in real-life settings. Our work aimed at developing a low-cost, print-and-play platform to acquire and analyse sEMG signals that can be arranged in a fully customized way, depending on the application and the users' needs. We produced 8-channel sEMG matrices to measure the muscular activity of the forearm using innovative nanoparticle-based inks to print the sensors embedded into each matrix using a commercial inkjet printer. Then, we acquired the multi-channel sEMG data from 12 participants while repeatedly performing twelve standard finger movements (six extensions and six flexions). Our results showed that inkjet printing-based sEMG signals ensured significant similarity values across repetitions in every participant, a large enough difference between movements (dissimilarity index above 0.2), and an overall classification accuracy of 93-95% for flexion and extension, respectively.

Early EEG responses to pre-electoral survey items reflect political attitudes and predict voting behavior.

Self-reports are conventionally used to measure political preferences, yet individuals may be unable or unwilling to report their political attitudes. Here, in 69 participants we compared implicit and explicit methods of political attitude assessment and focused our investigation on populist attitudes. Ahead of the 2019 European Parliament election, we recorded electroencephalography (EEG) from future voters while they completed a survey that measured levels of agreement on different political issues. An Implicit Association Test (IAT) was administered at the end of the recording session. Neural signals differed as a function of future vote for a populist or mainstream party and of whether survey items expressed populist or non-populist views. The combination of EEG responses and self-reported preferences predicted electoral choice better than traditional socio-demographic and ideological variables, while IAT scores were not a significant predictor. These findings suggest that measurements of brain activity can refine the assessment of socio-political attitudes, even when those attitudes are not based on traditional ideological divides.

Effects of cognitive workload on heart and locomotor rhythms coupling.

Different physiological signals could be coupled under specific conditions, in some cases related to pathologies or reductions in system complexity. Cardiac-locomotor synchronization (CLS) has been one of the most investigating coupling. The influence of a cognitive task on walking was investigated in dual-task experiments, but how different cognitive tasks may influence CLS has poorly been investigated. Twenty healthy subjects performed a dual-task walking (coupled with verbal fluency vs calculation) on a treadmill at three different speeds (comfortable speed CS; fast-speed: CS + 2 km/h; slow-speed: CS-2 km/h) while cardiac and walking rhythms were recorded using surface electrodes and a triaxial accelerometer, respectively. According to previous studies, we found a cognitive-motor interference for which cognitive performance was affected by motor exercise, but not vice-versa. We found a CLS at the baseline condition, at fast speed in both cognitive tasks, while at comfortable speed only for the verbal fluency task. In conclusion, the cardiac and locomotor rhythms were not coupled at slow speed and at comfortable speed during subtraction task. Cognitive performances generally increased at faster speed, when cardiac locomotor coupling was stronger.