Although accurate recognition of the idle state is essential for the application of brain-computer interfaces (BCIs) in real-world situations, it remains a challenging task due to the variability of the idle state. In this study, a novel algorithm was proposed for the idle state detection in a steady-state visual evoked potential (SSVEP)-based BCI. The proposed algorithm aims to solve the idle state detection problem by constructing a better model of the control states. For feature extraction, a maximum evoked response (MER) spatial filter was developed to extract neurophysiologically plausible SSVEP responses, by finding the combination of multi-channel electroencephalogram (EEG) signals that maximized the evoked responses while suppressing the unrelated background EEGs. The extracted SSVEP responses at the frequencies of both the attended and the unattended stimuli were then used to form feature vectors and a series of binary classifiers for recognition of each control state and the idle state were constructed. EEG data from nine subjects in a three-target SSVEP BCI experiment with a variety of idle state conditions were used to evaluate the proposed algorithm. Compared to the most popular canonical correlation analysis-based algorithm and the conventional power spectrum-based algorithm, the proposed algorithm outperformed them by achieving an offline control state classification accuracy of 88.0 ± 11.1% and idle state false positive rates (FPRs) ranging from 7.4 ± 5.6% to 14.2 ± 10.1%, depending on the specific idle state conditions. Moreover, the online simulation reported BCI performance close to practical use: 22.0 ± 2.9 out of the 24 control commands were correctly recognized and the FPRs achieved as low as approximately 0.5 event/min in the idle state conditions with eye open and 0.05 event/min in the idle state condition with eye closed. These results demonstrate the potential of the proposed algorithm for implementing practical SSVEP BCI systems.

Java's type system enforces exception-checking rules that stipulate a checked exception thrown by a method must be declared in the throws clause of the method. Software written in Java often invokes native methods through the use of the Java Native Interface (JNI). Java's type system, however, cannot enforce the same exception-checking rules on Java exceptions raised in native methods. This gap makes Java software potentially buggy and often difficult to debug when an exception is raised in native code. In this paper, we propose a complete static-analysis framework called JET to extend exception-checking rules even on native code. The framework has a two-stage design where the first stage throws away a large portion of irrelevant code so that the second stage, a fine-grained analysis, can concentrate on a small set of code for accurate bug finding. This design achieves both high efficiency and accuracy. We have applied JET on a set of benchmark programs with a total over 227K lines of source code and identified 12 inconsistent native-method exception declarations.

Software flaws in native methods may defeat Java's guarantees of safety and security. One common kind of flaws in native methods results from the discrepancy on how exceptions are handled in Java and in native methods. Unlike exceptions in Java, exceptions raised in the native code through the Java Native Interface (JNI) are not controlled by the Java Virtual Machine (JVM). Only after the native code finishes execution will the JVM's mechanism for exceptions take over. This discrepancy makes handling of JNI exceptions an error prone process and can cause serious security flaws in software written using the JNI.