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One of the major limitations of motor imagery (MI)based brain-computer interface (BCI) is its long calibration time. Due to between sessions/subjects variations in the properties of brain signals, typically a large amount of training data needs to be collected at the beginning of each session to calibrate the parameters of the BCI system for the target user. In this paper, we propose a novel transfer learning approach on the classification domain to reduce the calibration time without sacrificing the classification accuracy of MI-BCI. Thus, when only few subject-specific trials are available for training, the estimation of the classification parameters is improved by incorporating previously recorded data from other users. For this purpose, a regularization parameter is added to the objective function of the classifier to make the classification parameters as close as possible to the classification parameters of the previous users who have feature spaces similar to that of the target subject. In this study, a new similarity measure based on the kullback leibler divergence (KL) is used to measure similarity between two feature spaces obtained using subject-specific common spatial patterns (CSP). The proposed transfer learning approach is applied on the logistic regression classifier and evaluated using three datasets. The results showed that compared to the subject-specific classifier, the proposed weighted transfer learning classifier improved the classification results particularly when few subject-specific trials were available for training (p < 0.05). Importantly, this improvement was more pronounced for users with medium and poor accuracy. Moreover, the statistical results showed that the proposed weighted transfer learning classifier performed significantly better than the considered comparable baseline algorithms.

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Abstract-Previous research on kernel monitoring and protection widely relies on higher privileged system components, such as hardware virtualization extensions, to isolate security tools from potential kernel attacks. These approaches increase both the maintenance effort and the code base size of privileged system components, which consequently increases the risk of having security vulnerabilities. SKEE, which stands for Secure Kernellevel Execution Environment, solves this fundamental problem. SKEE is a novel system that provides an isolated lightweight execution environment at the same privilege level of the kernel. SKEE is designed for commodity ARM platforms. Its main goal is to allow secure monitoring and protection of the kernel without active involvement of higher privileged software.SKEE provides a set of novel techniques to guarantee isolation. It creates a protected address space that is not accessible to the kernel, which is challenging to achieve when both the kernel and the isolated environment share the same privilege level. SKEE solves this challenge by preventing the kernel from managing its own memory translation tables. Hence, the kernel is forced to switch to SKEE to modify the system's memory layout. In turn, SKEE verifies that the requested modification does not compromise the isolation of the protected address space. Switching from the OS kernel to SKEE exclusively passes through a well-controlled switch gate. This switch gate is carefully designed so that its execution sequence is atomic and deterministic. These properties combined guarantee that a potentially compromised kernel cannot exploit the switching sequence to compromise the isolation. If the kernel attempts to violate these properties, it will only cause the system to fail without exposing the protected address space. SKEE exclusively controls access permissions of the entire OS memory. Hence, it prevents attacks that attempt to inject unverified code into the kernel. Moreover, it can be easily extended to intercept other system events in order to support various intrusion detection and integrity verification tools. This paper presents a SKEE prototype that runs on both 32-bit ARMv7 and 64-bit ARMv8 architectures. Performance evaluation results demonstrate that SKEE is a practical solution for real world systems.