DOI: 10.5061/dryad.2v6wwpzz0
title: Data from: Fine-grained neural coding of bodies and body parts in human visual cortex

description: Body perception plays a fundamental role in social cognition. Yet, the
 neural mechanisms underlying this process in humans remain elusive given
 the spatiotemporal constraints of functional imaging. Here we present for
 the first time intracortical recordings of single- and multi-unit spiking
 activity in two epilepsy surgery patients in or near the extrastriate body
 area (EBA), a critical region for body perception. Our recordings revealed
 a strong preference for human bodies over a large range of control
 stimuli. Notably, body selectivity was driven by a distinct selectivity
 for body parts. The observed body selectivity generalized to
 non-photographic depictions of bodies including silhouettes and stick
 figures. Overall, our study provides unique neural data that bridge the
 gap between human neuroimaging and macaque electrophysiology studies,
 laying a solid foundation for computational models of human body
 processing.
description: All data were collected using 96-channel utah arrays implanted in
 human visual cortex, and recorded with a Neuroport system (Blackrock
 Neurotech).
description: # Supplementary Materials for \[Your Scientific Paper Title] This
 repository contains the supplementary data and source code used in our
 study. The materials are organized into two main sections: **Data** and
 **Source Code**. Below, you will find detailed information on how the data
 is structured and how to utilize the provided source code for analysis. ##
 Table of Contents * [Data](#data) * [Directory
 Structure](#directory-structure) * [File Naming
 Convention](#file-naming-convention) * [Data Format](#data-format) * [Data
 Fields](#data-fields) * [Source Code](#source-code) *
 [Overview](#overview) * [Entry Point](#entry-point) * [Data
 Preprocessing](#data-preprocessing) * [Additional
 Modules](#additional-modules) * [Usage Instructions](#usage-instructions)
 * [Dependencies](#dependencies) * [Contact](#contact) ## Data ###
 Directory Structure The data is organized into folders corresponding to
 different experiments. Each experiment folder contains subfolders for each
 patient (P1 and P2). Below is the hierarchical structure: ├──
 AbstractBodies │ ├── PatientP1 │ │ └── Session1_MUA.pkl │ └── PatientP2 │
 └── Session1_MUA.pkl ├── BodyParts │ ├── PatientP1 │ │ ├──
 Session1_MUA.pkl │ │ └── Session1_SUA.pkl │ └── PatientP2 │ ├──
 Session1_P1_MUA.pkl │ ├── Session1_P2_MUA.pkl │ └── Session1_SUA.pkl ├──
 Categorbodies │ ├── PatientP1 │ │ ├── Session1_MUA.pkl │ │ ├──
 Session_Silhouettes_P1_MUA.pkl │ │ └── Session_Silhouettes_P2_MUA.pkl │
 └── PatientP2 │ ├── Session1_P1_MUA.pkl │ └── Session1_P2_MUA.pkl ├──
 EBALocalizer │ └── PatientP2 │ └── Session1_P2_MUA.pkl └──
 RotationTolerance ├── PatientP1 │ └── Session1_MUA.pkl └── PatientP2 └──
 Session1_MUA.pkl ### File Naming Convention Each data file follows the
 naming pattern: SessionX_PY_.pkl * **SessionX**:
 Corresponds to the recording session number. * **PY**: Indicates the part
 of the session. Used for large files. * **Resolution**: Specifies the data
 resolution, either `MUA` (Multi-Unit Activity) or `SUA` (Single-Unit
 Activity). **Example:** `Session1_P1_MUA.pkl` ### Data Format All data
 files are in `.pkl` format and should be opened using Python. Each `.pkl`
 file contains a `trials` structure with entries corresponding to recorded
 trials. ### Data Fields Within each trial, the following keys are relevant
 for analysis: * **answer**: * `1` if the trial was correct. * `0`
 otherwise. * **MUAA**: * A dictionary with keys formatted as `elecX`,
 where `X` represents the recording electrode number from the Utah array
 (total of 96 electrodes). * Values are NumPy arrays containing spike times
 for each electrode. * **current_stimul_name**: * The name of the stimulus
 presented during the trial. ## Source Code ### Overview The source code
 provided is used to analyze the dataset. It is organized within the `Code`
 folder, which serves as the entry point for various analysis scripts. The
 analysis is conducted at different levels, including: * **Single
 Electrodes** * **Population Analysis** * **Decoding** * **V1 Modeling**
 ### Entry Point The primary entry point for the source code is the `Code`
 folder. This folder contains scripts tailored to analyze data at multiple
 levels. ### Data Preprocessing To understand how the data is preprocessed,
 refer to the `analysis_basic.py` script of the neurovisual package. This
 script outlines the fundamental preprocessing steps applied to the raw
 data before analysis together with single recording site analysis. ###
 Additional Modules Beyond the basic preprocessing, the source code
 includes extra modules that perform: * Population analysis:
 Multi-electrode interactions and patterns * Decoding analysis: Neural
 decoding and classification * V1 modeling: Primary visual cortex response
 modeling