Background
Non-invasive brain–computer interfaces (BCIs) have been developed for realizing natural bi-directional interaction between users and external robotic systems. However, the communication between users and BCI systems through artificial matching is a critical issue. Recently, BCIs have been developed to adopt intuitive decoding, which is the key to solving several problems such as a small number of classes and manually matching BCI commands with device control. Unfortunately, the advances in this area have been slow owing to the lack of large and uniform datasets. This study provides a large intuitive dataset for 11 different upper extremity movement tasks obtained during multiple recording sessions. The dataset includes 60-channel electroencephalography, 7-channel electromyography, and 4-channel electro-oculography of 25 healthy participants collected over 3-day sessions for a total of 82,500 trials across all the participants.
Findings
We validated our dataset via neurophysiological analysis. We observed clear sensorimotor de-/activation and spatial distribution related to real-movement and motor imagery, respectively. Furthermore, we demonstrated the consistency of the dataset by evaluating the classification performance of each session using a baseline machine learning method.
Conclusions
The dataset includes the data of multiple recording sessions, various classes within the single upper extremity, and multimodal signals. This work can be used to (i) compare the brain activities associated with real movement and imagination, (ii) improve the decoding performance, and (iii) analyze the differences among recording sessions. Hence, this study, as a Data Note, has focused on collecting data required for further advances in the BCI technology.
Stocks with high idiosyncratic volatility perform poorly relative to low idiosyncratic volatility stocks. We o↵er a novel explanation of this anomaly based on real options, which is consistent with earlier findings on idiosyncratic volatility (the positive contemporaneous relation between firm-level stock returns and idiosyncratic volatility). Our approach is based on introducing stochastic idiosyncratic cash flow risk into an equity valuation model of firms with growth options. Within our model, a firm's systematic risk depends on the delta of its growth option. The growth option's delta is lower when idiosyncratic volatility rises, driving down the firm's systematic risk and hence its expected return-firms with higher idiosyncratic volatility therefore have lower expected returns. Our model additionally o↵ers the following novel empirical predictions: (i) returns correlate positively with idiosyncratic volatility during intervals between large changes in idiosyncratic volatility (the switch e↵ect), and (ii) the anomalies and the switch e↵ect are stronger for firms with more real options and which undergo larger changes in idiosyncratic volatility. Empirical results support the predictions of our model.
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