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The start of the cue is often used to initiate the feature window used to control motor imagery (MI)-based brain-computer interface (BCI) systems. However, the time latency during an MI period varies between trials for each participant. Fixing the starting time point of MI features can lead to decreased system performance in MI-based BCI systems. To address this issue, we propose a novel correlation-based time window selection (CTWS) algorithm for MI-based BCIs. Specifically, the optimized reference signals for each class were selected based on correlation analysis and performance evaluation. Furthermore, the starting points of time windows for both training and testing samples were adjusted using correlation analysis. Finally, the feature extraction and classification algorithms were used to calculate the classification accuracy. With two datasets, the results demonstrate that the CTWS algorithm significantly improved the system performance when compared to directly using feature extraction approaches. Importantly, the average improvement in accuracy of the CTWS algorithm on the datasets of healthy participants and stroke patients was 16.72% and 5.24%, respectively when compared to traditional common spatial pattern (CSP) algorithm. In addition, the average accuracy increased 7.36% and 9.29%, respectively when the CTWS was used in conjunction with Sub-Alpha-Beta Log-Det Divergences (Sub-ABLD) algorithm. These findings suggest that the proposed CTWS algorithm holds promise as a general feature extraction approach for MI-based BCIs.

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Abstract-In this paper, the different temperature dependencies of hysteresis and eddy current losses of non-oriented Si-steel laminations are investigated. The measured iron loss results show that both the hysteresis and eddy current losses vary linearly with temperature between 40°C to 100°C, a typical temperature range of electrical machines. Varying rates of hysteresis and eddy current losses with the temperature are different and fluctuate with flux density and frequency. Based on this, an improved iron loss model which can consider temperature dependencies of hysteresis and eddy current losses separately is developed. Based on the improved iron loss model, the temperature influence on the iron loss can be fully considered by measuring iron losses at only two different temperatures. The investigation is experimentally validated by both the tests based on a ring specimen and an electrical machine.Index Terms-iron loss, eddy current loss, hysteresis loss, temperature dependency, electrical machines . In [29], only the temperature dependency of the eddy current loss is considered while the hysteresis loss is assumed to be not influenced by the temperature. In [30], the temperature influence on the total iron loss is simply modeled by introducing an equivalent temperature dependent coefficient which is a mix of temperature influences on both the hysteresis and eddy current losses. However in [27] and [28], it has shown experimentally that the hysteresis and eddy current losses have different temperature dependencies.The aim of this paper is to develop an iron loss model which can consider the temperature dependencies of the hysteresis and the eddy current losses separately. The iron losses at different flux density, frequency and temperature in non-oriented Si-steel laminations are measured firstly by the ring specimen test as will be described in Section II. In Section III, the accuracy of existing iron loss model having variable

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