Professional Experience
Klein's major area of research has been neurometrics and neurotechnology. Neuroscience with the development of non-invasive human brain imaging now uses human subject volunteers, The questions being researched get at some of the fundamental questions of what it means to be human and to have a mind. The revolution in technologies that has made this maturation possible extends from gene to hospital bed-side and is now referred to as neurotechnology. Some examples of neurotechnology include the CAT scanner, fMRI, Magnetoencephalography (MEG), Positron emission tomography, high-throughput genetic sequencing, brain proteomics and psychopharmaceuticals. These technologies also include neural modeling simulations, biological computers, and human-brain interfaces (prosthetics).
- 1967-1981: Assistant Prof. - Full Prof., Joint Science Dept., Claremont Colleges, Claremont, CA.
- 1972-1973: Visiting Faculty, Psychology Dept., Stanford University, Stanford, CA.
- 1974-1981: Visiting Associate, Division of Biology, Caltech, Pasadena, CA.
- 1978-1979: Sabbatical at Harvard and Smith-Kettlewell Institute of Visual Science, San Francisco, CA.
- 1981-1987: Professor, College of Optometry, University of Houston, Houston, Texas.
- 1987: Professor, University of California at Berkeley, School of Optometry
He currently (2011) is a thesis advisor for graduate students in the Berkeley Vision Processing Laboratory. He has over 190 published articles in the area of vision perception.
Klein has worked to developed new methods for obtaining and analyzing evoked EEG/MEG (electro- and magneto-encephalograpy) and related fMRI data that will provide needed spatio-temporal resolution. In order for M/EEG to become a widely used tool for analyzing brain function it is necessary to go from the sensor information (magnetic fields for MEG and electric potentials for EEG) to the identification (locations, orientations and time functions) of the multiple brain sources. He has helped to develop a novel set of stimuli that allows the collection of a much larger set of data than ever previously collected without increasing the data collection time. New algorithms overcome the "rotation problem" and to minimize the "mis-specification" problem so that the location, orientation and time functions of multiple cortical sources are identified.
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