Working memory modeling using inverse fuzzy relational approach

• Published in: Applied soft computing Vol. 83; p. 105591
• Main Authors: Ghosh, Lidia, Rakshit, Pratyusha, Konar, Amit
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.10.2019
• Subjects: Bidirectional associative memory; Fuzzy max–min inverse; Fuzzy relational technique; Short term memory; Unified particle swarm optimization; Working memory; Unified particle swarm optimization; Working memory; Short term memory; Fuzzy relational technique; Bidirectional associative memory; Fuzzy max–min inverse
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2019.105591

The paper proposes a novel approach to fuzzy modeling of human working memory (WM) using electroencephalographic (EEG) signals, acquired during human face encoding and recall experiments in connection with a face recognition problem. The EEG signals acquired from the short term memory (STM) during memory encoding instances are considered as the input of the proposed working memory model. On the other hand, the EEG response of the WM to visual stimuli acquired during WM recall instances are considered as the output of the proposed working memory model. The entire experiment is primarily divided into two phases. In the first phase, the WM of a human subject is modeled by a fuzzy implication relation, describing a mapping from the STM response (during encoding) to the WM responses (during recall) to visual stimuli. During STM encoding, the subject is visually presented with the full face stimulus of a person. During WM recall, four partial face stimuli of the same person (made familiar during encoding) are used for the subject to recall the respective full face. The second phase is undertaken to validate the WM model by visually stimulating the subject again with randomly selected partial faces of people, being familiar in the first phase and the WM EEG responses are recorded. The WM responses along with the WM model, developed in the first phase, are used to retrieve the STM information by using an inverse fuzzy (implication) relation. Besides WM modeling, another important contribution of the paper lies in devising a solution to the inverse fuzzy relation computation in the settings of an optimization problem. An error metric is then defined to measure the discrepancy between the model-predicted STM encoding pattern and the actual pattern encoded by the STM (as captured by the EEG signal during encoding in the first phase). Apparently, smaller the error magnitude better is the accuracy of the proposed model to effectively differentiate people with memory failures. Experimentally it is observed that the proposed model yields a very small error, in the order of 10−4, thus showing a high level of similarity between actual and model predicted STM response for all the healthy subjects. An experiment undertaken using eLORETA software confirms that the orbito-frontal cortex of prefrontal lobe is responsible for STM encoding whereas dorsolateral prefrontal region is responsible for WM recall. An analysis undertaken reveals that the proposed WM model produces the best response in the theta frequency band of EEG spectra, thus assuring the association of the theta frequency range in the face recognition task. Comparative analysis performed also substantiates that the proposed technique of computing max–min inverse fuzzy relation outperforms the existing techniques for inverse fuzzy computation, with a successful retrieval accuracy of 87.92%. The proposed study would find interesting applications to diagnose memory failures for people with Pre-frontal lobe amnesia. •This is one of the early works aiming at Working Memory modeling using Brain Signals.•Fuzzy inverse relational approach has been employed to develop the WM models.•Model performance analysis confirms the significance of the model in real human memory diagnosis.•The role of OFC in WM encoding and DLPFC in WM recall have been confirmed by experimental evidences.•Proposed model would find applications in WM diagnosis of patients with PFC amnesia.