2/12/2024 0 Comments Python decode mat file"eeg": The EEG signals are stored in this field. "data" is the field containing audio and EEG information. As described in the abstract, the directions of the competing speakers are drawn randomly from fifteen alternates. "azimuth" indicates the directions of the two competing speakers. You can directly extract the audio waveform from the "data" field. "l_audio" and "r_audio" indicate the file name of the left and right competing speakers, respectively. "left" indicates the subject was instructed to attend to the speaker on the left side. "attended_lr" indicates the relative attended direction (the relative direction of the attended speaker). "expinfo" is a table containing attention information. mat file into MATLAB or Python, you can access the data and experiment information through the "data" and "expinfo" fields. Note that as we manually removed some abnormal parts from the original EEG recordings besides ICA and filtering, the EEG duration of each trial is slightly different. mat files separately, including the EEG signal, attention information, and the competing speakers' information for each trial. The EEG signals were preprocessed in EEGLAB and sliced into trials.ĭata of each subject are stored in. The dataset includes EEG signals and audio stimuli. Note that there are 28 subjects participated in our experiments and the data of seven subjects were removed from further analysis due to device failure. Please contact the author at for additional information. This dataset provides the preprocessed EEG recordings and the aligned audio stimuli signals, as long as the attention information. In each trial, the subject was exposed to a pair of randomly selected stimuli, the directions of whom were randomly drawn from the 15 possible competing speaker directions, i.e., ☑35°, ☑20°, ☙0°, ☖0°, ±45°, ☓0°, ☑5° and 0°. The EEG data were recorded with the 32-channel EMOTIV Epoc Flex Saline system at a sampling rate of 128 Hz (downsampled from 1024 Hz) in a low-reverberant listening room.įor each subject, the experiment includes 32 trials. Unlike previous datasets (such as the KUL dataset), the locations of the two speakers are randomly drawn from fifteen alternatives.Īll subjects have given formal written consent approved by the Nanjing University ethical committee before the experiment and received financial compensation upon completion. If you have any questions, don’t hesitate to ask: mattgwwalker at is an auditory attention decoding dataset including EEG recordings of 21 subjects when they were instructed to attend to one of the two competing speakers at two different locations. Make sure you’ve installed Python and then it’s just as simple as running: You can download the program from github. So I went about putting those bits together into a Python script that automatically extracts out the key details of the emails and, most importantly, the attachments. But best of all Peter Fiskerstrand has already documented the majority of the parts of these. There is a Python library to read these structured files, and it declares itself to be fairly well mature. Also it’s the same structure as Microsoft Office uses for Word, Excel, etc. Internally, they’re like a fat-table based file system. msg files are in a file format called “COM stuctured storage OLE2 compound documents”. msg files but none of them allowed me to automate the extraction of the attachments. There are a few open source solutions to these. When I realised I had more than a thousand left to do, I thought automating this pain away was the only acceptable solution. In fact, I had to go through them all twice because it’s hard doing something right 42 times in a row without making an error. I started by hand to open each file and drag and drop the attachments. Even with Outlook, I don’t think there’s any way to search though them.īut I needed a file that was hidden somewhere in one of these. msg files are a pain to open (although incomplete open-source solutions do exist). Each of these files represents a single email, which includes both the text content and the attachments in the email. Summary: I have written a small free program to extract emails from Outlook’s.
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