A universal unanswered question in neuroscience and machine learning is whether computers can decode the patterns of the human brain. Multi-Voxels Pattern Analysis (MVPA) is a critical tool for addressing this question. However, there are two challenges in the previous MVPA methods, which include decreasing sparsity and noises in the extracted features and increasing the performance of prediction. In overcoming mentioned challenges, this paper proposes Anatomical Pattern Analysis (APA) for decoding visual stimuli in the human brain. This framework develops a novel anatomical feature extraction method and a new imbalance AdaBoost algorithm for binary classification. Further, it utilizes an Error-Correcting Output Codes (ECOC) method for multi-class prediction. APA can automatically detect active regions for each category of the visual stimuli. Moreover, it enables us to combine homogeneous datasets for applying advanced classification. Experimental studies on 4 visual categories (words, consonants, objects and scrambled photos) demonstrate that the proposed approach achieves superior performance to state-of-the-art methods.
Ever wondered why certain countries have certain colors in their flags, why it has certain symbols and what are the different patterns? We started digging through, checking a Wikipedia article on this topic. This was a good start but we wanted to go deeper so we manually started eye balling each flag to understand the patterns and symbols. We needed to figure out what we can extract from the flags and there were three prominent elements. Our task was then to go through each flag and note down all the distinct colors, prominent patterns and symbols.
This post would probably be the last in my series about merging R and ArcGIS. In August unfortunately I would have to work for real and I will not have time to play with R-Bridge any more. In this post I would like to present a toolbox to perform some introductory point pattern analysis in R through ArcGIS. Basically, I developed a toolbox to perform the tests I presented in my previous post about point pattern analysis. In there, you can find some theoretical concepts that you need to know to understand what this toolbox can do. I will start by introducing the sample dataset we are going to use, and then simply show the packages available.
You might think your memories are unique, but a study involving a Sherlock Holmes drama suggests the opposite. When people describe the episode, their brain activity patterns are almost exactly the same as each other's, for each scene. And there's also evidence that, when a person tells someone else about it, they implant that same activity into their brain as well. That's the implication of a groundbreaking experiment which, for the first time, has revealed that when we record and recount a shared experience, we use practically the same brain activity as each other, rather than everyone remembering and recalling events in random, individual ways. "We feel our memories are unique, but we see now that there's a lot in common between us in how we see and remember the world, even at the level of brain activity patterns," says Janice Chen at Princeton University.
Information mapping is a popular application of Multivoxel Pattern Analysis (MVPA) to fMRI. Information maps are constructed using the so called searchlight method, where the spherical multivoxel neighborhood of every voxel (i.e., a searchlight) in the brain is evaluated for the presence of task-relevant response patterns. Despite their widespread use, information maps present several challenges for interpretation. One such challenge has to do with inferring the size and shape of a multivoxel pattern from its signature on the information map. To address this issue, we formally examined the geometric basis of this mapping relationship. Based on geometric considerations, we show how and why small patterns (i.e., having smaller spatial extents) can produce a larger signature on the information map as compared to large patterns, independent of the size of the searchlight radius. Furthermore, we show that the number of informative searchlights over the brain increase as a function of searchlight radius, even in the complete absence of any multivariate response patterns. These properties are unrelated to the statistical capabilities of the pattern-analysis algorithms used but are obligatory geometric properties arising from using the searchlight procedure.