Biomedical subjects

Charles W Anderson

Publications and source records attributed to Charles W Anderson.

6 recordsLinked to original sources

PubMed2006

Geometric subspace methods and time-delay embedding for EEG artifact removal and classification.

Generalized singular-value decomposition is used to separate multichannel electroencephalogram (EEG) into components found by optimizing a signal-to-noise quotient. These components are used to filter out artifacts. Short-time principal components analysis of time-delay embedded EEG is used to represent windowed EEG data to classify EEG according to which mental task is being performed. Examples are presented of the filtering of various artifacts and results are shown of classification of EEG from five mental tasks using committees of decision trees.

Algorithms↗

PubMed2006

BCI Meeting 2005--workshop on BCI signal processing: feature extraction and translation.

This paper describes the outcome of discussions held during the Third International BCI Meeting at a workshop charged with reviewing and evaluating the current state of and issues relevant to brain-computer interface (BCI) feature extraction and translation. The issues discussed include a taxonomy of methods and applications, time-frequency spatial analysis, optimization schemes, the role of insight in analysis, adaptation, and methods for quantifying BCI feedback.

Algorithms↗

PubMed2006

Assessing students' ability to trace matter in dynamic systems in cell biology.

College-level biology courses contain many complex processes that are often taught and learned as detailed narratives. These processes can be better understood by perceiving them as dynamic systems that are governed by common fundamental principles. Conservation of matter is such a principle, and thus tracing matter is an essential step in learning to reason about biological processes. We present here multiple-choice questions that measure students' ability and inclination to trace matter through photosynthesis and cellular respiration. Data associated with each question come from students in a large undergraduate biology course that was undergoing a shift in instructional strategy toward making fundamental principles (such as tracing matter) a central theme. We also present findings from interviews with students in the course. Our data indicate that 1) many students are not using tracing matter as a tool to reason about biological processes, 2) students have particular difficulties tracing matter between systems and have a persistent tendency to interconvert matter and energy, and 3) instructional changes seem to be effective in promoting application of the tracing matter principle. Using these items as diagnostic tools allows instructors to be proactive in addressing students' misconceptions and ineffective reasoning.

Animals↗

PubMed2003

Comparison of linear, nonlinear, and feature selection methods for EEG signal classification.

The reliable operation of brain-computer interfaces (BCIs) based on spontaneous electroencephalogram (EEG) signals requires accurate classification of multichannel EEG. The design of EEG representations and classifiers for BCI are open research questions whose difficulty stems from the need to extract complex spatial and temporal patterns from noisy multidimensional time series obtained from EEG measurements. The high-dimensional and noisy nature of EEG may limit the advantage of nonlinear classification methods over linear ones. This paper reports the results of a linear (linear discriminant analysis) and two nonlinear classifiers (neural networks and support vector machines) applied to the classification of spontaneous EEG during five mental tasks, showing that nonlinear classifiers produce only slightly better classification results. An approach to feature selection based on genetic algorithms is also presented with preliminary results of application to EEG during finger movement.

Algorithms↗

PubMed2003

Linear and nonlinear methods for brain-computer interfaces.

At the recent Second International Meeting on Brain-Computer Interfaces (BCIs) held in June 2002 in Rensselaerville, NY, a formal debate was held on the pros and cons of linear and nonlinear methods in BCI research. Specific examples applying EEG data sets to linear and nonlinear methods are given and an overview of the various pros and cons of each approach is summarized. Overall, it was agreed that simplicity is generally best and, therefore, the use of linear methods is recommended wherever possible. It was also agreed that nonlinear methods in some applications can provide better results, particularly with complex and/or other very large data sets.

Algorithms↗

PubMed2002

Recognition memory for faces in schizophrenia patients and their first-degree relatives.

It has consistently been shown that schizophrenia patients are impaired in recognition memory for faces. However, studies have not examined the specificity of this deficit relative to other cognitive functions nor the relationship between this deficit and particular schizophrenia symptoms. In addition, no studies have examined recognition memory for faces in unaffected biological relatives of schizophrenia patients who likely share some of the genetic diathesis for this disorder without presenting the potential confounds of mentally ill study samples. The Faces subtests from the Wechsler Memory Scale-Third Edition were used to evaluate recognition memory for faces in 39 schizophrenia patients, 33 of their first-degree relatives and 56 normal controls. Both schizophrenia patients and their relatives were impaired, relative to control participants, in recognition memory for faces after partialing out group differences in spatial attention or verbal memory. Further, recognition memory for faces was associated with positive symptoms in the schizophrenia group and schizotypal personality traits in the relative group. These findings may have important implications for reducing etiological heterogeneity among schizophrenia populations, identifying disorder susceptibility among their relatives and furthering understanding of disorder etiology.

Adolescent↗