An intelligent way to play music by brain activity using brain computer interface

Arora, Bhawna; Choudhury, Tanupriya; Kumar, Praveen; Mukherjee,

doi:10.1109/ngct.2016.7877419

Cited by 8 publications

(4 citation statements)

References 14 publications

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“…Furthermore, the main diagonal includes the highest rates for all classes. Unlike the results reported in [7], stimuli 1-4 are not misclassified with their corresponding tempo-matched versions without lyrics (stimuli [11][12][13][14]. This may be due to the consideration of temporal patterns which are very evident in these signals, especially when speech is included in the stimuli [32].…”

Section: Resultscontrasting

confidence: 63%

A Statistical Inference Framework for Understanding Music-Related Brain Activity

Ntalampiras

Potamitis

2019

IEEE J. Sel. Top. Signal Process.

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Following the success in Music Information Retrieval (MIR), research is now steering towards understanding the relationship existing between brain activity and the music stimuli causing it. To this end, a new MIR topic has emerged, namely Music Imagery Information Retrieval, with its main scope being to bridge the gap existing between music stimuli and its respective brain activity. In this paper, the encephalographic modality was chosen to capture brain activity as it is more widely available since of-the-shelf devices recording such responses are already affordable unlike more expensive brain imaging techniques. After defining three tasks assessing different aspects of the specific problem (stimuli identification, group and meter classification), we present a common method to address them, which explores the temporal evolution of the acquired signals. In more detail, we rely on the parameters of linear time-invariant models extracted out of electroencephalographic responses to heterogeneous music stimuli. Subsequently, the probability density function of such parameters is estimated by hidden Markov models taking into account their succession in time. We report encouraging classification rates in the above-mentioned tasks suggesting the existence of an underlying relationship between music stimuli and their electroencephalographic responses.

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Section: Resultscontrasting

confidence: 63%

A Statistical Inference Framework for Understanding Music-Related Brain Activity

Ntalampiras

Potamitis

2019

IEEE J. Sel. Top. Signal Process.

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“…CNN is a feed-forward neural network mainly used for image recognition and processing. In this research, CNN is trained for the automatic segmentation of brain tumors [17,18]. A hierarchical clustering segmentation effectively performs multi-category segmentation tasks.…”

Section: Brain Tumor Segmentationmentioning

confidence: 99%

Dense Hierarchical CNN – A Unified Approach for Brain Tumor Segmentation

Sille¹,

Choudhury²,

Chauhan³

et al. 2021

RIA

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Brain tumor segmentation is an essential and challenging task because of the heterogeneous nature of neoplastic tissue in spatial and imaging techniques. Manual segmentation of the tumor in MRI images is prone to error and time-consuming tasks. An efficient segmentation mechanism is vital to the accurate classification and segmentation of tumorous cells. This study presents an efficient hierarchical clustering-based dense CNN approach for accurately classifying and segmenting the brain tumor cells in MRI images. The research focuses on improving the efficiency of the segmentation algorithms by considering the qualitative measures such as the dice score coefficient using quantitative parameters such as mean square error and peak signal to noise ratio. The experimental analysis states the efficacy and prominence of the proposed technique compared to other models are tabulated within the paper.

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“…During this attack remaining cognitive functions are remain unaffected [1]. To overcome such condition some of the remarkable research products developed by using EEG signals were stated here they are, Gaming controller [2], Smart Home Controller [3], TV channel Controller [4], wheelchair [5]- [9], Keyboard controller [10]- [12], Eye tracking Glass [13], Music Player [14]- [15] , Web browser Controller [16], Arm Movement Controller [17]- [19], Mouse Controller [20], Virtual Car Driving System [21]. In this research work we compared and studied the performances of traditional method with hybrid technique using FFNN (with benchmark Levenberg Marquardt back propagation algorithm and hybrid Grey wolf Optimization algorithm).…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of EEG Signals using Conventional and Hybrid Artificial Neural Network

2019

IJRTE

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Brain Computer Interface (BCI) using Electrooencephalogram (EEG) is one of the versatile tools to measure the brain thoughts and convert them to operate the external devices in the deficiency of biological controls. These techniques are used to develop the rehabilitative devices for the individual person who affected with locked in syndrome (LIS). The main reason for LIS is due to death of motor neurons. To overcome the real world problem we conduct our experiment with eight normal patients for four tasks using three electrode system and ADI T26 bio amplifier with Lab chart. Four task signals are applied to statistical method to retrieve the twenty two features and trained with the feed forward neural network (FFNN) and feed forward neural network with wolf Grey optimization algorithm (FFNNWGOA) to see network model which was more perfectly supported to identify the tasks. The study showed that statistical features through feed forward neural network with grey wolf optimized algorithm classifier produced maximum performances of 94.06% compared to other feed forward neural network classifier model and also we identified that optimized features demonstrated maximum performances in minimum time duration during training in all the following twenty trials.

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An intelligent way to play music by brain activity using brain computer interface

Cited by 8 publications

References 14 publications

A Statistical Inference Framework for Understanding Music-Related Brain Activity

A Statistical Inference Framework for Understanding Music-Related Brain Activity

Dense Hierarchical CNN – A Unified Approach for Brain Tumor Segmentation

Performance Analysis of EEG Signals using Conventional and Hybrid Artificial Neural Network

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