Understanding effects of observing affordance-driven action during motor imagery through EEG analysis.

The aim of this paper is to investigate the impact of observing affordance-driven action during motor imagery. Affordance-driven action refers to actions that are initiated based on the properties of objects and the possibilities they offer for interaction. Action observation (AO) and motor imagery (MI) are two forms of motor simulation that can influence motor responses. We examined combined AO + MI, where participants simultaneously engaged in AO and MI. Two different kinds of combined AO + MI were employed. Participants imagined and observed the same affordance-driven action during congruent AO + MI, whereas in incongruent AO + MI, participants imagined the actual affordance-driven action while observing a distracting affordance involving the same object. EEG data were analyzed for the N2 component of event-related potential (ERP). Our study found that the N2 ERP became more negative during congruent AO + MI, indicating strong affordance-related activity. The maximum source current density (0.00611 µ A/mm 2 ) using Low-Resolution Electromagnetic Tomography (LORETA) was observed during congruent AO + MI in brain areas responsible for planning motoric actions. This is consistent with prefrontal cortex and premotor cortex activity for AO + MI reported in the literature. The stronger neural activity observed during congruent AO + MI suggests that affordance-driven actions hold promise for neurorehabilitation.

Glove-Net: Enhancing Grasp Classification with Multisensory Data and Deep Learning Approach.

Grasp classification is pivotal for understanding human interactions with objects, with wide-ranging applications in robotics, prosthetics, and rehabilitation. This study introduces a novel methodology utilizing a multisensory data glove to capture intricate grasp dynamics, including finger posture bending angles and fingertip forces. Our dataset comprises data collected from 10 participants engaging in grasp trials with 24 objects using the YCB object set. We evaluate classification performance under three scenarios: utilizing grasp posture alone, utilizing grasp force alone, and combining both modalities. We propose Glove-Net, a hybrid CNN-BiLSTM architecture for classifying grasp patterns within our dataset, aiming to harness the unique advantages offered by both CNNs and BiLSTM networks. This model seamlessly integrates CNNs' spatial feature extraction capabilities with the temporal sequence learning strengths inherent in BiLSTM networks, effectively addressing the intricate dependencies present within our grasping data. Our study includes findings from an extensive ablation study aimed at optimizing model configurations and hyperparameters. We quantify and compare the classification accuracy across these scenarios: CNN achieved 88.09%, 69.38%, and 93.51% testing accuracies for posture-only, force-only, and combined data, respectively. LSTM exhibited accuracies of 86.02%, 70.52%, and 92.19% for the same scenarios. Notably, the hybrid CNN-BiLSTM proposed model demonstrated superior performance with accuracies of 90.83%, 73.12%, and 98.75% across the respective scenarios. Through rigorous numerical experimentation, our results underscore the significance of multimodal grasp classification and highlight the efficacy of the proposed hybrid Glove-Net architectures in leveraging multisensory data for precise grasp recognition. These insights advance understanding of human-machine interaction and hold promise for diverse real-world applications.

Motor imagery and mental fatigue: inter-relationship and EEG based estimation.

Even though it has long been felt that psychological state influences the performance of brain-computer interfaces (BCI), formal analysis to support this hypothesis has been scant. This study investigates the inter-relationship between motor imagery (MI) and mental fatigue using EEG: a. whether prolonged sequences of MI produce mental fatigue and b. whether mental fatigue affects MI EEG class separability. Eleven participants participated in the MI experiment, 5 of which quit in the middle because of experiencing high fatigue. The growth of fatigue was monitored using the Kernel Partial Least Square (KPLS) algorithm on the remaining 6 participants which shows that MI induces substantial mental fatigue. Statistical analysis of the effect of fatigue on motor imagery performance shows that high fatigue level significantly decreases MI EEG separability. Collectively, these results portray an MI-fatigue inter-connection, emphasizing the necessity of developing adaptive MI BCI by tracking mental fatigue.

Neural Correlates of Motor Imagery during Action Observation in Affordance-based Actions: Preliminary Results.

Object affordance, a characterization of the different functionalities of an object, refers to an object's numerous possibilities of interaction. It has a significant part to play in priming motoric actions which depends on the actor's spontaneous neurological behaviour. Action Observation (AO) and Motor Imagery (MI) also lead to the stimulation of motor system. In fact, AO and MI result in activation of overlapping brain areas as the actual motor task. AO combined with MI (referred to as AO+MI) initiates higher cortical activity in comparison with either MI or AO alone. In this paper, we investigate the influence of affordance driven motor priming during AO, MI and AO + MI. Source current density as an EEG parameter is estimated by Low Resolution Electromagnetic Tomography (LORETA). Our results demonstrate that affordance driven motor priming during AO+MI indicates stronger electrophysiological and behavioural changes. This is evident from the N2 ERP component. Further, the current source density (in brain regions associated with motor planning) during affordance driven AO+MI is found to be maximum.

Adaptive feature extraction in EEG-based motor imagery BCI: tracking mental fatigue.

OBJECTIVE: Electroencephalogram (EEG) signals are non-stationary. This could be due to internal fluctuation of brain states such as fatigue, frustration, etc. This necessitates the development of adaptive brain-computer interfaces (BCI) whose performance does not deteriorate significantly with the adversary change in the cognitive state. In this paper, we put forward an unsupervised adaptive scheme to adapt the feature extractor of motor imagery (MI) BCIs by tracking the fatigue level of the user. APPROACH: Eleven subjects participated in the study during which they accomplished MI tasks while self-reporting their perceived levels of mental fatigue. Out of the 11 subjects, only six completed the whole experiment, while the others quit in the middle because of experiencing high fatigue. The adaptive feature extractor is attained through the adaptation of the common spatial patterns (CSP), one of the most popular feature extraction algorithms in EEG-based BCIs. The proposed method was analyzed in two ways: offline and in near real-time. The separability of the MI EEG features extracted by the proposed adaptive CSP (ADCSP) has been compared with that by the conventional CSP (C-CSP) and another CSP based adaptive method (ACSP) in terms of: Davies Bouldin index (DBI), Fisher score (FS) and Dunn's index (DI). MAIN RESULTS: Experimental results show significant improvement in the separability of MI EEG features extracted by ADCSP as compared to that by C-CSP and ACSP. SIGNIFICANCE: Collectively, the results of the experiments in this study suggest that adapting CSP based on mental fatigue can improve the class separability of MI EEG features.

Recognition of grasp types through principal components of DWT based EMG features.

With the advancement in machine learning and signal processing techniques, electromyogram (EMG) signals have increasingly gained importance in man-machine interaction. Multifingered hand prostheses using surface EMG for control has appeared in the market. However, EMG based control is still rudimentary, being limited to a few hand postures based on higher number of EMG channels. Moreover, control is non-intuitive, in the sense that the user is required to learn to associate muscle remnants actions to unrelated posture of the prosthesis. Herein lies the promise of a low channel EMG based grasp classification architecture for development of an embedded intelligent prosthetic controller. This paper reports classification of six grasp types used during 70% of daily living activities based on two channel forearm EMG. A feature vector through principal component analysis of discrete wavelet transform coefficients based features of the EMG signal is derived. Classification is through radial basis function kernel based support vector machine following preprocessing and maximum voluntary contraction normalization of EMG signals. 10-fold cross validation is done. We have achieved an average recognition rate of 97.5%.