There is increasing interest in real-time brain-computer interfaces (BCIs) for the passive monitoring of human cognitive state, including cognitive workload. Too often, however, effective BCIs based on machine learning techniques may function as “black boxes” that are difficult to analyze or interpret. In an effort toward more interpretable BCIs, we studied a family of N-back working memory tasks using a machine learning model, Gaussian Process Regression (GPR), which was both powerful and amenable to analysis. Participants performed the N-back task with three stimulus variants, auditory-verbal, visual-spatial, and visual-numeric, each at three working memory loads. GPR models were trained and tested on EEG data from all three task variants combined, in an effort to identify a model that could be predictive of mental workload demand regardless of stimulus modality. To provide a comparison for GPR performance, a model was additionally trained using multiple linear regression (MLR). The GPR model was effective when trained on individual participant EEG data, resulting in an average standardized mean squared error (sMSE) between true and predicted N-back levels of 0.44. In comparison, the MLR model using the same data resulted in an average sMSE of 0.55. We additionally demonstrate how GPR can be used to identify which EEG features are relevant for prediction of cognitive workload in an individual participant. A fraction of EEG features accounted for the majority of the model’s predictive power; using only the top 25% of features performed nearly as well as using 100% of features. Subsets of features identified by linear models (ANOVA) were not as efficient as subsets identified by GPR. This raises the possibility of BCIs that require fewer model features while capturing all of the information needed to achieve high predictive accuracy.
Background: We prepared and evaluated training and test materials for an assessment of text mining methods in molecular biology. The goal of the assessment was to evaluate the ability of automated systems to generate a list of unique gene identifiers from PubMed abstracts for the three model organisms Fly, Mouse, and Yeast. This paper describes the preparation and evaluation of answer keys for training and testing. These consisted of lists of normalized gene names found in the abstracts, generated by adapting the gene list for the full journal articles found in the model organism databases. For the training dataset, the gene list was pruned automatically to remove gene names not found in the abstract; for the testing dataset, it was further refined by manual annotation by annotators provided with guidelines. A critical step in interpreting the results of an assessment is to evaluate the quality of the data preparation. We did this by careful assessment of interannotator agreement and the use of answer pooling of participant results to improve the quality of the final testing dataset.
Turtle visual cortex has three layers and receives direct input from the dorsolateral geniculate complex of the thalamus. The outer layer 1 contains several populations of interneurons, but their physiological properties have not been characterized. This study used intracellular recording methods followed by filling with Neurobiotin to characterize the morphology and physiology of two populations of layer 1 interneurons. Subpial cells have somata positioned in the outer third of layer 1 and dendrites confined within the band of geniculate afferents that runs from lateral to medial across visual cortex. Their dendrites are composed of a sequence of many beads or varicosities separated by intervaricose segments. They have membrane time constants of tau(o) = 45.5 +/- 5.2 ms and electrotonic lengths of 1.1 +/- 0.2. Subpial cells show spike rate adaptation in response to intracellular current pulses. Stellate cells have somata located in the inner two-thirds of layer 1 and, less frequently, in layers 2 and 3. Their dendrites extend in a stellate configuration across the cortex. They are smooth or sparsely spiny, but never bear distinct varicosities. They have membrane time constants of tau(o) = 155.1 +/- 12 ms and electrotonic lengths of 3.8 +/- 0.5. They show little spike rate adaptation in response to intracellular current pulses. The positions of the two populations of cells in visual cortex and their physiological properties suggest that subpial cells may participate in a feedforward inhibitory pathway to pyramidal cells, whereas stellate cells are involved in feedback inhibition to pyramidal cells.
The massive volume of intrusion detection system (IDS) alarms generated on large networks, and the resulting need for laborintensive security analysis of the text-based IDS alarm logs, has recently brought into question the cost-effectiveness of IDSs. In particular, when host-based IDSs are used to monitor an organization's internal networks, the majority of the resulting alarms represent legitimate, automated system administration. Because of the absence of ground truth about known attacks, we propose an unsupervised, anomaly-based method for automatically distinguishing alarms that are potentially generated by malicious insider attacks, from the repetitive and temporally structured legitimate system-administration alarms. The majority of previous work in this area has used heuristic and statistical filtering techniques to discard a relatively large proportion of alarms in the final presentation to the security analyst, which is a potentially dangerous practice. Instead, we demonstrate the use of a typicality measure to visualize the apparent risk associated with alarms, while retaining information about the temporal context of the entire alarm stream for the analyst to view. The relevance of the statistical method is examined by comparing the results to a set of analyst-curated alarms from an operational environment.
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