Robotic Arm with Brain – Computer Interfacing

Sunny, T.D.; Aparna, T.; Neethu, P.; Venkateswaran, Jayendran; Vishnupriya, V.; Vyas, Preeti

doi:10.1016/j.protcy.2016.05.241

Cited by 41 publications

(11 citation statements)

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“…These methods are based on the use of simultaneous neuronal population activities from a given cortical region in order to drive the devices that can help patients with specific dysfunctions or deficits to improve their quality of life. Most BCI technologies are designed to address motor-related dysfunctions such as motor prosthesis or driving external palliative devices such as cursors (Trejo et al, 2006 ) or robotic arms (Sunny et al, 2016 ), among others. Little research has been directed to develop the BCI devices that rely on decoding higher-order cognitive processes such as attention (Andersen et al, 2010 ; Astrand et al, 2014b ), due to the fact that such a cognitive content is internally generated by the neuronal signals that are often multiplexed with different types of information, including sensory and motor information.…”

Section: Is Persistent Attention-related Information Actually Persistent?mentioning

confidence: 99%

Revisiting Persistent Neuronal Activity During Covert Spatial Attention

Amengual

Hamed

2021

Front. Neural Circuits

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Persistent activity has been observed in the prefrontal cortex (PFC), in particular during the delay periods of visual attention tasks. Classical approaches based on the average activity over multiple trials have revealed that such an activity encodes the information about the attentional instruction provided in such tasks. However, single-trial approaches have shown that activity in this area is rather sparse than persistent and highly heterogeneous not only within the trials but also between the different trials. Thus, this observation raised the question of how persistent the actually persistent attention-related prefrontal activity is and how it contributes to spatial attention. In this paper, we review recent evidence of precisely deconstructing the persistence of the neural activity in the PFC in the context of attention orienting. The inclusion of machine-learning methods for decoding the information reveals that attention orienting is a highly dynamic process, possessing intrinsic oscillatory dynamics working at multiple timescales spanning from milliseconds to minutes. Dimensionality reduction methods further show that this persistent activity dynamically incorporates multiple sources of information. This novel framework reflects a high complexity in the neural representation of the attention-related information in the PFC, and how its computational organization predicts behavior.

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Section: Is Persistent Attention-related Information Actually Persistent?mentioning

confidence: 99%

Revisiting Persistent Neuronal Activity During Covert Spatial Attention

Amengual

Hamed

2021

Front. Neural Circuits

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“…Already in the 14th century, Leonardo da Vinci made blueprints of robotic arms. 2 A robotic arm resembles a human arm. It consists of segments that are connected by joints.…”

Section: Introductionmentioning

confidence: 99%

Path planning of robotic arm based on deep reinforcement learning algorithm

Al‐Gabalawy

2022

Adv Control Appl

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Robotic Arms are used in many fields due to their high accuracy. A robotic arm has the advantage of solving the same tasks as a human arm because of its similar structure and working repetitively and independently of any human.Setting up a robotic arm's task requires the engineer to plan a path for solving the task. Using an approach with reinforcement learning to make the robotic arm learn the path independently has not been feasible due to robotic arm tasks' sparse rewards. A recently successful method of using Reinforcement Learning is the concept of Hindsight Experience Replay. This article extends the difficulty of existing tasks to two new tasks and examines Hindsight Experience Replay's performance on these tasks. In the first experiment, the robotic arm has to move a ball towards a point that is far outside of its reach, similar to golf.In the second experiment, the task is to toss a ball into a box outside its reach, similar to basketball. Results show that vanilla Hindsight Experience Replay performs poorly on these tasks. Further research on solving the tasks with improvements to Hindsight Experience Replay, like Hindsight Goal Generation and Energy-Based Hindsight Experience Prioritization, is required to solve them further.

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“…The brain–computer interface (BCI) or brain–machine interface (BMI) is a direct communication pathway for controlling external devices by discriminating brain signals [ 1 , 2 ]. BCIs have been widely researched and applied for many practical systems [ 3 , 4 , 5 , 6 ]. There are various methods for acquiring brain signals for BCIs such as electroencephalography (EEG), magnetoencephalography (MEG), and functional magnetic resonance imaging (fMRI).…”

Section: Introductionmentioning

confidence: 99%

Steady-State Visual Evoked Potential Classification Using Complex Valued Convolutional Neural Networks

Ikeda

Washizawa

2021

Sensors

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The steady-state visual evoked potential (SSVEP), which is a kind of event-related potential in electroencephalograms (EEGs), has been applied to brain–computer interfaces (BCIs). SSVEP-based BCIs currently perform the best in terms of information transfer rate (ITR) among various BCI implementation methods. Canonical component analysis (CCA) or spectrum estimation, such as the Fourier transform, and their extensions have been used to extract features of SSVEPs. However, these signal extraction methods have a limitation in the available stimulation frequency; thus, the number of commands is limited. In this paper, we propose a complex valued convolutional neural network (CVCNN) to overcome the limitation of SSVEP-based BCIs. The experimental results demonstrate that the proposed method overcomes the limitation of the stimulation frequency, and it outperforms conventional SSVEP feature extraction methods.

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Robotic Arm with Brain – Computer Interfacing

Cited by 41 publications

References 0 publications

Revisiting Persistent Neuronal Activity During Covert Spatial Attention

Revisiting Persistent Neuronal Activity During Covert Spatial Attention

Path planning of robotic arm based on deep reinforcement learning algorithm

Steady-State Visual Evoked Potential Classification Using Complex Valued Convolutional Neural Networks

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