Search Results for author: Isaak Kavasidis

Found 5 papers, 2 papers with code

Correct block-design experiments mitigate temporal correlation bias in EEG classification

1 code implementation25 Nov 2020 Simone Palazzo, Concetto Spampinato, Joseph Schmidt, Isaak Kavasidis, Daniela Giordano, Mubarak Shah

We argue that the reason why Li et al. [1] observe such high correlation in EEG data is their unconventional experimental design and settings that violate the basic cognitive neuroscience design recommendations, first and foremost the one of limiting the experiments' duration, as instead done in [2].

Classification EEG +3

Decoding Brain Representations by Multimodal Learning of Neural Activity and Visual Features

no code implementations25 Oct 2018 Simone Palazzo, Concetto Spampinato, Isaak Kavasidis, Daniela Giordano, Joseph Schmidt, Mubarak Shah

After verifying that visual information can be extracted from EEG data, we introduce a multimodal approach that uses deep image and EEG encoders, trained in a siamese configuration, for learning a joint manifold that maximizes a compatibility measure between visual features and brain representations.

Classification EEG +4

Generative Adversarial Networks Conditioned by Brain Signals

no code implementations ICCV 2017 Simone Palazzo, Concetto Spampinato, Isaak Kavasidis, Daniela Giordano, Mubarak Shah

In this work, we build on the latter class of approaches and investigate the possibility of driving and conditioning the image generation process by means of brain signals recorded, through an electroencephalograph (EEG), while users look at images from a set of 40 ImageNet object categories with the objective of generating the seen images.

EEG Electroencephalogram (EEG) +1

Deep Learning Human Mind for Automated Visual Classification

2 code implementations CVPR 2017 Concetto Spampinato, Simone Palazzo, Isaak Kavasidis, Daniela Giordano, Mubarak Shah, Nasim Souly

In particular, we employ EEG data evoked by visual object stimuli combined with Recurrent Neural Networks (RNN) to learn a discriminative brain activity manifold of visual categories.

Classification EEG +4

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